research paper on edge computing

• DOI: 10.1109/ACCESS.2020.2991734
• Corpus ID: 218676558

An Overview on Edge Computing Research

• Keyan Cao , Yefan Liu , +1 author Qimeng Sun
• Published in IEEE Access 6 May 2020
• Computer Science, Engineering

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Edge Computing with Artificial Intelligence: A Machine Learning Perspective

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Information & Contributors

Bibliometrics & citations, view options, 1 introduction, 1.1 edge computing, 1.2 artificial intelligence, 1.3 combination of edge computing and artificial intelligence, 1.4 review of existing surveys, 1.5 our contributions.

2 Introduction of Edge Computing

2.1 why we need edge computing, 2.1.1 the big data era caused by internet of things., 2.1.2 more stringent requirements of network stability and response speed., 2.1.3 privacy and security., 2.2 the definition of edge computing.

2.3 Problems Studied in Edge Computing

2.3.1 computing offloading., 2.3.2 resource allocation., 2.3.3 privacy and security., 2.4 summary, 3 when edge computing meets artificial intelligence, 3.1 motivations of combining edge computing and artificial intelligence.

3.1.1 Edge Computing Benefits Artificial Intelligence.

3.1.2 Artificial Intelligence Benefits Edge Computing.

3.2 introduction of artificial intelligence algorithms in edge computing, 3.2.1 traditional machine learning., 3.2.2 deep learning., 3.2.3 reinforcement learning and deep reinforcement learning., 3.2.4 federated learning., 3.2.5 evolutionary algorithms., 3.3 artificial intelligence solutions for optimizing edge computing.

3.3.1 Computing Offloading Optimization.

3.3.2 non-computation offloading methods to reduce energy consumption., 3.3.3 security of edge computing., 3.3.4 data privacy., 3.3.5 resource allocation optimization., 3.4 summary, 4 application of artificial intelligence under edge computing.

4.1 Smart City

4.1.1 security of city., 4.1.2 urban healthcare., 4.1.3 urban energy management..

4.2 Smart Manufacturing

4.3 internet of vehicles.

4.3.1 Optimizing Task Offloading and Resource Allocation.

4.3.2 improving on-board experience., 4.3.3 improving vehicle intelligence., 4.3.4 challenges., 4.4 summary, 5 conclusion.

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New York, NY, United States

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• Edge computing
• artificial intelligence
• machine learning

Funding Sources

• National Natural Science Foundation of China
• China Postdoctoral Science Foundation
• Fundamental Research Funds for the Central Universities of China
• BNRist Program

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Edge computing

A grounded theory study

• Regular Paper
• Open access
• Published: 20 July 2022
• Volume 104 , pages 2711–2747, ( 2022 )

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• Jorge Pérez 1 ,
• Jessica Díaz   ORCID: orcid.org/0000-0001-6738-9370 1 ,
• Javier Berrocal 2 ,
• Ramón López-Viana 1 , 3 &
• Ángel González-Prieto 4  

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IoT edge computing is a new computing paradigm “in the IoT domain” for performing calculations and processing at the edge of the network, closer to the user and the source of the data. This paradigm is relatively recent, and, together with cloud and fog computing, there may be some confusion about its meaning and implications. This paper aims to help practitioners and researchers better understand what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm. We conducted a survey using a semi-structured in-depth questionnaire to collect qualitative data from relevant stakeholders from 29 multinational companies and qualitatively analyzed these data using the Constructivist Grounded Theory (Charmaz) method. Several researchers participated in the coding process (collaborative coding). To ensure consensus on the constructs that support the theory and thus improve the rigor of qualitative research, we conducted an intercoder agreement analysis. From the analysis, we have derived a substantive and analytic theory of what companies perceive about IoT edge computing, its benefits and challenges. The theory is substantive in that the scope of validity refers to the 29 surveys processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the information capture process and the products resulting from the analysis of this information is publicly available. This study aims to strengthen the evidence and support practitioners in making better informed decisions about why companies are adopting edge computing and the current challenges they face. Additionally, the testing theory phase shows that the results are aligned with the ISO/IEC TR 30164 standard.

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1 Introduction

During the last few years, we have witnessed a massive increase in the number of devices connected to the Internet. In particular, it is expected that the number of connected devices will be more than three times the global population by 2023. Indeed, Machine-To-Machine (M2M) connections will correspond to half of such connected devices (up to 14.7 billion M2M connections) in that year [ 7 ]. This deployment has fostered the deployment of the Internet of Things (IoT) paradigm, a comprehensive network of intelligent objects that have the capacity to automatically organize, share information, data, and resources, react and act in the face of situations and changes in the environment [ 35 ]. This has led to an exponential growth in the amount of data traffic flowing through the network.

The computation of shared data can be intensive and can usually not be completed by the IoT devices themselves, due to limited resources (memory, battery, etc.) [ 2 ]. The deployment and exploitation of the cloud paradigm has helped companies address this capacity limitation by offloading intensive computing tasks to the cloud. However, this offloading has a penalty on the quality of service (QoS) offered, such as the increase in latency imposed by the distance between the cloud and the end devices, the network overhead, and the increase in the security and privacy risk that this offloading entails.

To reduce this penalty, over the last few years, the edge computing paradigm has been proposed. Edge computing allows the offloading of the computing task to nodes closer to the end devices (at one hop from them). Therefore, these tasks are closer to the source of data and the consumer of data, increasing the quality of service offered.

However, the exact definition and coverage of edge computing are unclear. Some researchers indicate that edge computing addresses the offloading of computing tasks from the cloud to the last hop before smart devices, others indicate that it only covers the set of devices at one hop from end devices, some also include IoT devices, etc. [ 60 ]. In addition, different researches recommend the application of edge computing for different domains, applications with a strict response time, for those who want to reduce the infrastructure cost or to improve the privacy management. Therefore, there is currently a lack of consensus on the specific coverage, targeted domains, and benefits of edge computing.

For this paradigm, to have an adoption by the industry similar to that of other proposals, such as cloud computing, all stakeholders should share a common vision, clearly knowing the different elements and concepts involved in it, what are the main problems that allow them to address, what are the main benefits provided, and for which domains.

This paper presents an analysis of the vision of edge computing in the industry. To this end, 29 international companies have been surveyed to identify what they understand by edge computing, what problems it addresses, and what benefits it provides. This analysis allows us to bring the vision of the business world closer to the academy, allowing both of them to better focus their efforts to improve its adoption.

The remainder of the paper is structured as follows. Section 2 analyzes the main concerns that led us to carry out this study. Section 3 is devoted to the research methodology applied in this work. The development and final description of the created theory on IoT edge computing is presented in Sect. 4 . In Sect. 5 we analyze the threads to validity of this theory and its limitations. A description of existing work related to our proposal is provided in Sect. 6 , information that is complemented by the information shown in B . Finally, Sect. 7 draws the main conclusions of this work.

2 Motivation

Cloud computing is an architecture based on accessing centralized computing resources ubiquitously and on demand by making use of the network. This paradigm was standardized by the National Institute of Standards and Technology (NIST). The concept of cloud computing is to have hugely powerful servers in data centers connected to the network. The resources of cloud servers are then virtualized and offered to clients. Cloud computing has been the de facto solution over the past decade. One of the main reasons for its rise and wide adoption is the clarity and common vision that the entire industry has about the advantages and disadvantages that it provides [ 22 ]. For Edge Computing (EC), and other similar paradigms, to be successfully incorporated by the industry, it is necessary for them to clearly share its vision and the benefits it brings.

Specifically, there are some key requirements of QoS-stringent IoT applications that cannot be met only by applying a pure cloud paradigm, such as response time, cost, sensitivity, data volume, bandwidth limitation, resilience, etc. [ 27 ]. The Internet Research Task Force (IRTF) in its draft on IoT Edge Challenges and Functions [ 31 ] concluded that these limitations should be overcome by applying EC. The basis for EC is to use different computing devices closer to the end user to distribute the workload of the application to them [ 36 ]. However, there is no consensus on the definition of this paradigm in the literature, nor on the border with other closely related paradigms, which jeopardize the adoption of these paradigms by the industry [ 60 ].

ISO defines the term Edge Computing (EC) as “a form of distributed computing in which processing and storage takes place on a set of networked machines which are near the edge, where the nearness is defined by the system’s requirements” [ 30 ]. Nevertheless, the NIST also introduces a couple of closely related terms, Fog Computing (FC) and Mist Computing (MC), which makes it more difficult to clearly identify the borders of EC and its benefits. FC is defined by the OpenFog Consortium as “a horizontal system-level architecture that distributes computing, storage, and networking functions closer to the user along a cloud-to-thing continuum” [ 20 ]. Moreover, FC can be multitiered: fog nodes do not need to be placed at a single point or a single network tier and can be placed on multiple tiers. Instead, MC is “a lightweight computation distribution proposal that resides directly at the edge of the network fabric, bringing the FC layer closer to the smart end devices” .

Different proposals have been proposed with concrete nuances to support these paradigms. For instance, the ETSI’s Multi-access Edge Computing (MEC) [ 9 ]. MEC is an architecture based on the provision of computing and storage resources closer to users, similar to FC, but with resources directly connected to a 4G or 5G base station. In addition, the concept of Cloudlet proposes to bring cloud servers to the edge, instead of computing and storage resources closer to the edge with intermediate devices [ 49 ]. Aside from the previously defined architectures, some works present different proposals contributing to the EC. In [ 4 ], the authors present Mobile Edge Clouds, a three-tier architecture for IoT that contains IoT devices, a middle tier that contains mobile devices, which can run services, and the cloud. During the execution time, the bottom tier requests a service to the middle tier, which can be executed either within the middle tier or it can be offloaded to the top tier. The osmotic computing platform is another proposal that is also closely related to edge computing [ 56 ]. Osmotic computing bases its model on microelements . These microelements are deployed and provisioned on the basis of the concept of osmosis: initially, microelements are deployed in the cloud. As requests for microelements arrive at the infrastructure, the osmotic computing platform is in charge of detecting the requirements from the requests. If these requirements require that the microelement be deployed on the edge, the platform automatically provisions them at the appropriate edge node and maintains them as long as required.

Regarding MC, some works define different proposals detailing its own architecture [ 42 , 53 ], while others define it as a layer of fog or edge computing [ 28 ]. Thus, although it is possible to combine MC with other architectures [ 28 , 42 , 53 ], it is not clear whether such a combination is allowed by design or not. Other architectures aim at combining different architectures into a single one by merging their proposals and concepts. This is the case of [ 3 ], which proposes a combination of MEC and FC. In the literature, EC in some cases is described as a concept implemented in FC, MC, or MEC as described by K. Dolui et al. [ 11 ]. In other cases, it is used interchangeably as pointed out by [ 60 ].

Therefore, there is a plethora of approaches that define frameworks to apply the edge computing paradigm. However, each approach addresses the paradigm from a different point of view, defines different elements, and tries to meet different goals and challenges. Therefore, there is no clear and unanimous agreement on what EC is, which is crucial for its adoption in the industry, and more importantly, this view should come from the industry to share a common language and better transmit the benefits of this paradigm.

3 Research methodology

This paper presents empirical research on practicing IoT edge computing. It is based mainly on the constructivism model as an underlying philosophy (epistemological and ontological positions) [ 12 ]. Constructivism or interpretivism states that scientific knowledge cannot be separated from its human context and that a phenomenon can be fully understood by considering the perspectives and the context of the involved participants. Therefore, the most suitable methods to support this approach are those collecting rich qualitative data, from which theories (tied to the context under study) may emerge.

One of these methods is Grounded Theory (GT), which aims at the iterative development of a theory from qualitative data [ 16 ] and encourages deep immersion in the data [ 46 ]. “In grounded theory, initial analysis of the data begins without any preconceived categories. As interesting patterns emerge, the researcher repeatedly compares these with existing data, and collects more data to support or refute the emerging theory” [ 12 ]. Thus, GT is adequate for our purposes, and according to our philosophical stance, we used Constructivist Grounded Theory (aka Charmaz’s GT variant [ 6 ]). Specifically, we applied a novel process that extends the Charmaz GT variant to allow multiple researchers to participate in the coding process, i.e., collaborative coding , while ensuring consensus on the constructs that support the theory and, thus, improving the rigor of qualitative research (cf. [ 10 ]). To this end, we conducted an analysis of the intercoder agreement (ICA) to measure the extent to which different raters assign the same precise value (code or category) for each item being rated (qualitative data item or quotations) [ 14 ].

GT allows in-depth analysis of the phenomenon to be studied, that is, the perception that companies have of IoT edge computing. Once the data had been collected through a survey, GT is the methodology (in the field of qualitative research) that seemed most appropriate. Others, such as ethnographic or action research, require the researcher to enter the context to be studied for a prolonged period of time, which is unfeasible for this study due to the circumstances surrounding it, that is, software companies jealous of the way they work. We did not find appropriate to use focus groups (given that the individuals work for different organizations) or content/temathic analysis (subsumed in GT but which do not generate any theory).

3.1 Initial research questions

We began by asking what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm.

3.2 Data collection

GT involves iteratively performing interleaved rounds of qualitative data collection and analysis to lead to a theory (e.g., concepts, categories, patterns) [ 47 ]. The selection of participants is also iterative and can be considered a combination of “convenience sampling” as we are restricted to organizations and relevant stakeholders to which we had access; “theoretical sampling”, in the sense that we chose which data to collect based on the concepts or categories that were relevant to the emerging theory, i.e., data from organizations that have been adopting IoT edge computing; and “maximum variation sampling”, in the sense that we tried to choose highly diverse people and organizations in our sample, strengthening the transferability of our theory.

According to the purposed sampling strategy , we initially collected data from a set of participants from several leading international organizations in the Internet of Things domain, which are currently committee members of the Master’s Degree in Distributed and Embedded Systems Software Footnote 1 and Master’s Degree in IoT Footnote 2 at Universidad Politécnica de Madrid (Spain), and international industrial contacts of the Universidad de Extremadura (Spain). Then we moved on to theoretical sampling and iteratively collected more data based on the concepts or categories that were relevant to the emerging theory until the ICA value exceeded a given threshold and the theoretical saturation was reached. Table  1 lists the organizations involved in the study, their ID, scope (international or national), size Footnote 3 , business core, and role and experience of the respondent. A total of 29 responses were collected from a open-ended questionnaire available in https://es.surveymonkey.com/r/PMWD7ZM .

3.3 Qualitative data analysis

GT is a technique for iteratively developing theory from qualitative data [ 16 ] that encourages a deep immersion in the data [ 46 ]. “In grounded theory, initial analysis of the data begins without any preconceived categories. As interesting patterns emerge, the researcher repeatedly compares these with existing data and collects more data to support or refute the emerging theory” [ 12 ]. To conduct a constructivist GT, we will follow the following steps: initial/open coding, selection of core categories, selective coding, sorting, theoretical coding, and write-up. These steps are detailed in the next section.

4 A Theory on IoT edge computing

This section describes a theory on how the IoT industry perceives the edge computing paradigm, as well as the benefits they expect from adopting this paradigm and the challenges they face. To analyze the data from the survey responses of 29 companies and construct the theory, we followed the steps described in the previous section. It is important to keep in mind two concerns: i) the theory to be developed is a substantive theory; and ii) the theory is about how companies perceive the IoT edge computing paradigm and not so much how the paradigm has been defined in other scientific sources and/or standards.

4.1 Initial/Open coding

This activity aims to discover the concepts underlying the data and instantiate them in the form of codes. Thereby, at each iteration of the open coding, n documents of the survey are analyzed, that is, chopped into quotations that are assigned to either a previously discovered code or a new one that emerges to capture a new concept.

4.1.1 Iteration 1

In the first iteration of the open coding process, researchers R1, R2, and R3 analyzed 6 documents. R1 created a codebook with 29 codes that was subsequently refined by R2 and R3. As a by-product of this process, 40 codes were discovered and divided into 7 semantic domains (denoted by S1, S2,..., S7) (see Table  2 ).

After completing the coding process, Krippendorff’s \(\alpha \) coefficients [ 26 , 32 ] were computed (see also [ 18 ] for a thorough introduction to these techniques). Specifically, \(Cu\text {-}\alpha \) Footnote 4 and \(cu\text {-}\alpha \) Footnote 5 coefficients were computed and their values are shown in Table 3 . As we can observe from this table, the value of the global coefficient \(Cu\text {-}\alpha \) did not reach the acceptable threshold of 0.8, as fixed in the literature [ 32 ]. For this reason, a review meeting was necessary to discuss disagreements and the application criteria of the different codes. The results of this meeting are documented in the disagreements diary file of the open coding folder in the public repository.

To highlight problematic codes, we used the coefficients \(cu\text {-}\alpha \) calculated per semantic domain. For Table 3 , we observe that domain S3 had a remarkably low value of the coefficient \(cu\text {-}\alpha \) . A thorough look at the particular codes within S3 shows that this domain includes codes related to the functionality of the system. This is particularly a fuzzy domain in which several concepts can be confused. During the review meeting, clarifications about these codes were necessary to avoid misconceptions. After this, a new codebook was released. In this new version, memos and comments were added, and a code was removed, so 39 codes (and 7 semantic domains) proceeded to the second iteration of the open coding.

4.1.2 Iteration 2

Researchers R1, R2, and R3 analyzed six other documents. Since the coders agreed on a common codebook in the previous iteration, we can expect a greater agreement that materializes as a higher value of ICA. As a by-product of this second iteration, 8 new codes arose, leading to a new version of the codebook with 47 codes and 7 semantic domains (see Table  4 ).

The ICA values for this second iteration are shown in Table 5 . From the results of this table, we observe that after this refinement of the codebook, \(Cu\text {-}\alpha \) reaches the acceptable threshold of agreement. In this way, the open coding process can stop: There exists consensus in the interpretation of the codes presented in the codebook, and we can proceed with the selection of core categories and selective coding.

4.2 Selection of core categories

In this activity, R1 and R2 selected the core categories, that is, the most relevant codes from the 47 codes obtained in the open coding. To this end, we focused on the groundedness of the codes and semantic domains (i.e., the number of quotations coded by a code) and the density of the codes and semantic domains (i.e., the number of relationships between codes, that is, the cooccurrence of codes in the same quotation). Table  6 shows these values. The detailed analysis is documented in the selection of core categories file of the selection of core categories folder in the public repository. As a result of the analysis, four semantic domains (S1, S2, S3 and S6) and 29 codes were selected for the next activity. This codebook is available in the selection of core categories - codebook file of the public repository.

4.3 Selective coding

This is an inductive-deductive process in which new data are labeled with the codes of selected categories (semantic domains). The coders only focused on the core categories, but the number and definition of their inner codes were modified according to the analysis of new data. The researchers R1, R2 and R3 analyzed 6 documents using S1, S2, S3 and S6, which comprise a total of 29 codes. After coding, 9 codes were added to the codebook, representing a total of 38 core codes (see Appendix A). The results of the ICA coefficients obtained after coding are shown in Table  7 .

As we can observe from this table, the value of \(Cu\text {-}\alpha \) reached the acceptable reliability threshold of 0.8. This evidences that there exists a consensus among the coders on the meaning and limits of the codes within the core categories. Additionally, the coders also agreed that adding new data did not lead to new information, so the theoretical saturation had been reached. Therefore, since after this first iteration, the value of \(Cu\text {-}\alpha \) was compelling and the coders agreed that the theoretical saturation had been reached, the GT process could proceed to the next activity.

4.4 Sorting procedure

From the analysis of the memos together with the co-occurrence tables, we drew the relationships between the different categories (see Fig. 1 ). The core categories are boxed, while the font size of each category, as well as the thickness of the lines that relate them, correspond to the groundedness of semantic domains and the density of codes, respectively.

Relations between Categories

Scope of the theory

4.5 Theoretical coding

Theoretical coding is defined as “the property of coding and constant comparative analysis that yields the conceptual relationship between categories and their properties as they emerge” [ 17 ]. According to Gregor’s taxonomy [ 19 ], we develop an analytic theory: “Theories of this type include descriptions and conceptualizations of ’what is”’ . Taxonomies, classifications, and ontologies, as defined by Gruber [ 21 ], are also included. In fact, Gregor says “Some examples of grounded theory can also be examples of Type I theory, where the grounded theory method gives rise to a description of categories of interest.” . Type I refers to “Analytic theories analyze ’what is’ as opposed to explaining causality or attempting predictive generalizations” . These types of theory are valuable when little is known about the phenomena they describe. This is the case for the edge computing paradigm, which is relatively new. That is, the theory to be built will answer “What is edge computing?” We do not attempt to answer questions related to “why edge computing is used” (explanation theory), nor do we intend to develop mathematical/probabilistic models to support predictions (prediction theory), nor do we intend to describe “how to do” things (design and action theory or prescription theory).

To develop the theory, we follow the following steps, which are thoroughly described in the following sections.

Determining the scope of the theory

Defining the constructs of the theory

Defining the propositions of the theory

Providing explanations to justify the theory

Testing the theory

4.5.1 Determining the scope of the theory

Figure  2 shows, using UML 2.0, the elements and relations that determine the scope of this theory. We describe the theory scope through four archetype classes: Actor, Technology, Activity, and Software_System ( “An actor applies technologies to perform certain activities on an (existing or planned) software system” [ 52 ]. The four archetype classes have been represented as abstract classifiers (classes), and the relationships between these archetypes are as stated in [ 52 ]. We added several classifiers (subclasses) to indicate that the activities of IoT edge systems are performed on the edge side (see the enumerated type Location). Specifically, the subclass IoT_Edge_Computing represents a technology understood as a set of skills, techniques, methods, and processes, all specialized for the IoT edge computing paradigm. The subclass Activity_IoT_Edge represents an activity performed at the edge; in addition, this class has been declared as active, since, by its very nature, its instances will have their own control flows. Finally, the subclass IoT_Edge_Software_System represents a software system in the IoT Edge computing domain.

Since the construction of the theory was based on the qualitative analysis of data obtained from a set of surveys of 29 companies in the sector, this theory must necessarily be limited to a substantive (local) theory, as opposed to what could be a formal (all-inclusive) theory. However, the scope of the theory will be revealed during the testing phase.

4.5.2 Defining the constructs of the theory

The constructs have been derived from the codes associated with the core categories. The codes of each core category are described in Appendix A. Table  8 shows the constructs and the code(s) from which they are derived.

Figure  3 shows the relations between the elements of the scope and the constructs. Regarding this figure, class Device _ in _ the _ Edge and its subclasses represent the constructs C1 to C4. The classes Sensor and Actuator represent the construct C5. Distributed_Architecture represents the construct C6. The classes Advantage and Problem represent constructs C7 and C8, respectively. Construct C9 (represented by the classifier Activity_IoT_Edge) is an artifice (it is not central to the theory, although it is part of the scope of the theory) that allows us to establish two levels of abstraction in the operations performed by an IoT_Edge_Software_System. These high-level operations generate the benefits of the IoT_Edge_Computing technology. For example, the storage and analytics in the Device _ in _ the _ Edge , and the filtering and artificial intelligence techniques enable local data processing and avoid sending raw data to fog/cloud, gaining better bandwidth throughput. Table  9 relates the constructs to the classifiers in Fig.  3 .

Constructs & scope of the theory

4.5.3 Defining the propositions of the theory

The propositions of the theory are derived from the relationships between the constructs that make up the theory. In this sense, we extract the relationships described in Fig.  3 as propositions. We characterize each proposition by means of three elements: i) the actual textual statement of the proposition; ii) its formalization by means of the OCL language; and iii) an excerpt, as an example, obtained from the surveys that mention this relationship. The code of each proposition is composed of the letter P, followed by an order number and optionally in square brackets the classifiers to which it relates, a comma character, and the constructs. Additionally, we use a hyphen to indicate several items of a range and & for a sequence of items. Thus, the code P1 [1 &6, C1-C4 & C9], refers to Proposition 1, which states the relationship between classifiers 1 and 6 ( Device _ in _ the _ Edge and Activity_IoT_Edge) that support the relationship of constructs C1-C4 and C9.

P1 [1 &6, C1-C4 & C9]. A device located at the edge (i.e., an instance of one of the subclasses of the class Device _ in _ the _ Edge ) /participates in the execution of one or more Activity_IoT_Edge. Since classifier 6 (see Table  9 ) is an artifice, there is no excerpt to support it. The OCL syntax is as follows:

P2 [1 &6, C1-C4 & C9]. An Activity_IoT_Edge invokes the operations of one or more instances of one of the subclasses of Device _ in _ the _ Edge to carry out its responsibilities. Since classifier 6 (see Table  9 ) is an artifice, there is no excerpt to support it.

P3 [1 &3, C1-C4 & C6]. A device located at the edge (that is, an instance of one of the subclasses of the class Device _ in _ the _ Edge ) participates in a Distributed_Architecture. The OCL syntax and excerpts are as follows:

P4. An IoT_Edge_Software_System has one and only one architecture, and this architecture is unique. This relationship is established by knowledge of the problem domain: every software system has an associated architecture, whatever its type. The OCL syntax is as follows:

P5 [4 &6, C9 & C7]. The execution of Activity_IoT_Edge instances generates advantages defined by the enumeration type named Benefit. The OCL syntax and excerpts are as follows:

P6 [1 &5, C1-C4 & C8]. IoT_Edge_Computing technology (represented by the IoT_Edge_Computing classifier) has a number of problems (defined by the Challenge type) to be solved, the solution of which would bring new benefits (NewAdvantage classifier). The OCL syntax and excerpts are as follows:

In both cases, reference is made to different challenges faced by using IoT Edge technology, such as security, scalability, vendor lock-in, and deployment time.

P7 [1 &2, C1-C4 & C5]. An instance of type Device _ in _ the _ Edge (or any of its subtypes) is connected to an arbitrary number of instances of the types Sensor and Actuator. The OCL syntax and excerpts are as follows:

P8. The use of Edge computing techniques (represented by the IoT_Edge_Computing classifier), such as the use of containers, faces some of the challenges (described in the enumeration type Challenge), such as scalability. The OCL syntax and excerpts are as follows:

P9. The use of Edge computing techniques (represented by the IoT_Edge_Computing classifier), such as downlinks of wireless communication networks, enables some of the benefits (described in the enumeration type Benefit) such as save energy .

The rest of the associations, indicated in Fig.  3 (other than those of generalization), refer to the scope of the theory (Fig.  2 ) and are, therefore, outside the scope of the propositions. For example, the relations “define” and “execute”. The generalization (inheritance) relations are implicitly shown in the propositions listed above (indicated by the OCL expressions relating to navigation between classifiers).

4.5.4 Providing explanations to justify the theory

An explanation is a relation between constructs and other categories that are not central enough to become constructs. The code of each explanation comprises the letter E, followed by a number referring to its order, and, optionally between brackets, the number of the proposition related to the explanation separated by hyphens. In this way, the code “E1 [1-2]” refers to Explanation 1 about Propositions 1 and 2.

E1 [1-2]. An activity (instance of the classifier Activity_IoT_Edge) may involve several instances of the classifier Device _ in _ the _ Edge and one of the latter may intervene in several activities. These activities are high-level operations whose results are sensible to being analyzed to measure the benefits of this paradigm. However, it is complicated to analyze these benefits in operations of a smaller scope carried out by a single type of device.

E2 [3]. The very nature of an IoT application makes it a strong candidate to be based on an architecture with distributed and interconnected elements. In this architecture, many types (instances of the subclasses) of Device _ in _ the _ Edge may appear arbitrarily.

E3 [4]. It may seem that the same architecture can support different IoT edge software systems. However, this rarely occurs since the number and types of components involved are typically characteristic of a particular system. However, several IoT edge software systems may share a reference architecture (comprised of a reference model and an architectural style).

E4 [5]. The explanation of some of the benefits captured by the enumeration type Benefit is the following (it is an abductive reasoning):

E4.1 “better_bandwidth_throughput”. Since part of the post-processing of the data ingestion process is done locally, a large amount of bandwidth is saved by transmitting only the data in “cooked” format instead of “raw” format.

E4.2 “better_performance”. If all the context (elements needed to carry out a computation) is saved locally, then much time is saved in service requests to other computational nodes, leading to an increase in performance.

E4.3 “better_user_experience”. Since the performance of the system has been improved, better response times may be expected to user queries, leading to an improvement of the quality of the user experience.

E4.4 “customer_has_the_control_of_his/her_data”. When we transmit the data in “cooked” format, the customer retains control of the “raw” data that were generated in the IoT on the Edge devices and were not sent through the network.

E4.5 “greater_efficiency”. We should understand efficiency as the fundamental reduction in the amount of wasted resources that are used to produce a given number of goods or services. In other words, to produce the same results, fewer bandwidth requests and service requests to remote notes are needed.

E4.6 “less_cloud_overhead”. Since a large amount of processing is done locally, the cloud is not responsible for this task.

E4.7 “less_response_time”. This is strongly related to the time invested in communications. When we reduce this time due to local processing, we also decrease the response time observed by the user.

E4.8 “lower_latency”. The latency is related to the use of the network. If remote service requests are needed, we must send the request through the network and wait for a response from the server. These lead to an increase in the waiting time to get a response, i.e. the latency of the net. In this manner, the fewer service requests that are issued, the lower the global latency observed.

E5 [6]. The explanation of some of the benefits captured by the enumeration type Challenge is the following (it is an abductive reasoning):

E5.1. “complexity”. The complexity of these systems is determined by: i) the heterogeneity of the devices to be connected, regarding their properties and functions, but also in the definition of their interfaces; ii) the requirements of real-time operation; iii) the costs of developing and maintaining the system to achieve a permanent operation; iv) the financial and human consequences of a malfunctioning of the system.

E5.2 “deployment_time”. The time needed to deploy the system in production environments must be as low as possible if we want to compete with similar products. This also implies that we must deploy new functionalities and fix errors quickly. The complexity of these systems, as pointed out previously, as well as the necessary automation of the CI and CD process, requires a continuous effort to update on the air infrastructure (hardware and software) to exploit the potential of its new functionalities.

E5.3 “latency”. Under strict real-time conditions, the latency of the network remains an issue. Maybe the 6G system will cushion this problem, but removing it is quite unlike. The higher the traffic flow, the higher the expected demand. This phenomenon is analogous to the well-known problem with RAM memory, in which programs tend to occupy all the available space.

E5.4 “maintenance_cost”. This cost refers not only to hardware infrastructure (devices and networks to be maintained), but also to the software infrastructures that have to be updated and the applications that require more and more resources.

E5.5 “reliability”. These types of applications often have strong requirements on the expected reliability. In this context, reliability must be understood as the “degree to which a system, product, or component performs specified functions under specified conditions for a specified period of time” [ 29 ]. The lack of this attribute may jeopardize customers and their resources. However, to get reliability, one must balance cost and risk. It is not possible, in very complex systems, to achieve a reliability of 100%, but reaching levels close to this value is feasible.

E5.6 “scalability”. In general terms, a system is scalable if it can grow to adapt to new and more exigent demands of service, without requiring a change of architecture and only increasing the invested amount of resources. For instance, an intelligence system for agricultural tasks is scalable if it can be adapted to new croplands (with a new area to be screened with new types of crops) by only increasing the number of resources (devices, communications) without altering the architecture or the implementation.

E5.7 “security &privacity”. In IoT systems in domains such as health, the privacy of the used data and the mechanisms applied to meet these constraints are crucial for the success of the system.

E5.8 “time_to_market”. The speed with which a new version of an IoT edge software system is released is crucial to the survival of any organization.

E6 [7]. A device located at the edge will be connected with sensors (to obtain data from the context) and actuators (to modify the context). The device is fed with sensor data, processes them locally or remotely, and uses the results to command the actuators.

E7 [8-9]. The use of techniques from the IoT Edge computer domain (containers, virtual environments -machines, networks, servers, downlinks of wireless communication networks, orchestration coordination) allows developers to address problems like scalability and to obtain benefits like saving energy or response time.

4.5.5 Testing the theory

The last step of the theory-building process involves examining the validity of the theory. To this aim, we examine the following elements:

The data from the surveys not used in the previous steps to contrast how the theory fits to the new data.

The standard ISO/IEC TR 30164 (Internet of things (IoT) - Edge computing) to validate the alignment of the theory developed with this standard.

The clarity and precision of the elements that are part of the theory.

The extent to which a theory has been validated.

The scope of the theory.

Analysis of the remaining surveys. The remaining 11 surveys were analyzed to test whether the propositions established in Sect. 4.5.3 are aligned or contradict the data contained in the surveys. Recall that, as the previously analyzed surveys, only the answers to questions 7, 10, 13, 14, 15, 16, 19, and 21 were parsed.

This analysis confirms that no new constructs emerged, apart from those described in Sect. 4.5.2 , no new relations were needed and therefore no new propositions were added. Furthermore, the previously formulated propositions were validated, clarifying the conclusions.

Analysis of the standard ISO/IEC TR 30164. Sect.  1 (Scope) of that document says “This document describes the common concepts, terminologies, characteristics, use cases and technologies (including data management, coordination, processing, network functionality, heterogeneous computing, security, hardware/software optimization) of edge computing for loT systems applications” .

For this reason, it makes sense to compare this standard with our theory to validate the theory. The main conclusions raised were the following.

The main motivations for edge computing pointed out by the standard (latency, disconnected operations, need to minimize the volume of data transmitted upstream, and data providence) are reflected in the theory developed (Benefit::lower latency, Device _ in _ the _ Edge ::disconnected mode, Benefit::better bandwidth throughput, and Challenge::security & privacy).

Our theory encompasses the main classifiers (constructs) indicated in the conceptual viewpoint of the standard as follows. We associate the classifier IoT_System of the standard with the classifier IoT_Edge_Software_System of the theory, as well as the classifier IoT_ Component of the standard with the classifier Device _ in _ the _ Edge of the theory. However, it is worth mentioning that the theory does not distinguish between physical and digital entities, whereas the standard does include this distinction.

The functional viewpoint of the standard claims that “An edge computing entity can have but is not limited to the functions mentioned in 6.3.” The functions described in Section 6.3 of the standard are subsumed in the methods of the Device _ in _ the _ Edge classifier. We should understand that these functions are those extracted from the surveys and do not represent an exhaustive list of the functions that can be assigned to an IoT_Edge_Software_System.

Regarding the deployment viewpoint, the standard defines two deployment models: three levels vs. four layers. In both models, a distributed architecture underlies, as pointed out in the theory developed.

In summary, for the aforementioned reasons, we consider that the theory is perfectly aligned with the standard.

Clarity and precision. The constructs and propositions of a theory should be clear and precise so that they are understandable, internally consistent, and free of ambiguities. In our case, the definitions and descriptions of both constructs and propositions have been expressed in UML and, in the case of the propositions, also in OCL. The semantics of each of the elements that appear in the UML/OCL diagrams are described in their respective specifications [ 41 ] and [ 40 ] (see also [ 48 ]). Due to the formal language applied, there is no room for ambiguity or inconsistency, problems that would have been detected by the tool used to draw the diagrams [ 37 ].

It is worth mentioning that the semantics of some of the operations/attributes defined in some of the classifiers may be misleading, but if we would like to clarify them, this information would be artificially added from the researchers’ knowledge, since it is not reflected in the documentation analyzed. In this sense, we limited ourselves to define/characterize the elements that arise in the theory only based on the data extracted from the surveys, trying to not include any extra knowledge.

Extent to which a theory has been validated. Following [ 52 ], we must differentiate between two terms: scope of interest and scope of validity of a theory. In our case, the scope of interest was explained in Sect. 4.5.1 .

On the other hand, quoting [ 52 ], “The theory’s scope of validity refers to that part of the scope of interest in which the theory has actually been validated. The scope of validity of a theory is the accumulated scopes of validity of the results of the studies that have tested the theory, or the studies from which the theory has been generated” . In our case, the scope of validity is for the 18 surveys used to generate the theory, plus the remaining surveys (11) used to validate it.

The scope of the theory. In general, this concept refers to the fact that conditions must be explicitly and clearly specified, so that the domain or situations in which the theory should be (dis)confirmed and applied are clear. In our case, the scope was set in Sect. 4.5.1 and graphically depicted in Fig. 2 . Roughly speaking, the theory can be applied to IoT edge software systems.

4.6 Discussion

As noted by Glaser [ 15 ], “The task of the GT researcher is to generate a theory within the chosen data boundaries, not a formal theory” . The same author also highlights “The researcher, if using the classical GT method, is set up to write – and must – to conclude a substantive GT. He/she should stop, write.”

Independently of the description in the standard ISO/IEC TR 30164, the theory developed in this work is based on the perception that the professionals involved in the surveying process have about what edge computing is. Indeed, the standard is relatively recent (April 2020), so its adoption by the industry, if finally reached, will take some time.

IoT edge computing is a computational paradigm within the IoT framework characterized by the aim of moving the computations as close as possible to the data source. This computation is held in edge devices that frequently have severe limitations of computing speed and storage resources. These limitations are common to several types of devices (gateways, servers, microcontrollers, etc.) and condition the functionality that they can host (filtering, video processing, storage and analytics, etc.). As a result of carrying out the computation at the edge, we obtain a set of benefits (better performance, less response time, greater efficiency, etc.). However, a set of problems related to this paradigm must also be addressed if it intends to be applicable to the IoT framework (deployment time, reliability, scalability, etc.). Finally, we would like to mention that all the applications supported by this paradigm must present a highly distributed architecture with interconnected remote nodes in different topologies.

From the analysis conducted, it is possible to deduce that all the companies identify that there are several dimensions that must be taken into account, or that are affected by, the application of edge computing in the design of IoT applications. The dimensions identified by the vast majority of companies are computing, networking, functionality, and technology. Indeed, all of these dimensions are affected because all organizations highlight that the application of a distributed architectural design is crucial for this paradigm and that greater control of this distribution is also necessary to achieve the desired QoS. However, with respect to the challenges highlighted, a lower consensus may initially be seen, but a more thorough analysis shows otherwise. The organizations interviewed identify different challenges, but they are closely related. Thus, an important challenge is the complexity in the management of these applications, which is highly related to other challenges such as the need to automate this management and the deployment of functionalities, which also lead to better control of the scalability, reliability, and maintenance cost of the systems.

Furthermore, the deployment and monitoring of highly distributed applications, where quality can be affected by several highly related dimensions, entails greater complexity in management. Companies demand tools that allow them to automate this process, to detail their needs in a simpler way, to automate how applications should scale in these highly distributed environments, and how the operational cost can be kept under control. Therefore, one of the key aspects that can be deduced from this study is that methodologies, techniques, and tools are needed in this direction, so that organizations can apply this paradigm more boldly and confidently. In this regard, the GT study has detected some of the challenges that have not been described in the literature so far, such as those related to the delivery and deployment of IoT edge applications. This could indicate that further research in that area is required to introduce or adapt already existing paradigms that have been proven successful when dealing with highly distributed workloads such as DevOps or GitOps. In such a case, new research is required to analyze which other benefits and challenges arise when adopting these paradigms in the domain of IoT edge computing.

5 Threats to validity and reliability. Limitations

Criteria for judging the quality of the research design are the key to establish the validity, that is, the accuracy of the findings and the reliability, i.e., the consistency of the procedures and the researcher’s approach, of most empirical research [ 8 , 59 ]. We considered the quality criteria defined by Lincoln and Guba’s [ 34 ] for qualitative research as follows:

Credibility is also referred to as trustworthiness, i.e., the extent to which conclusions are supported by rich, multivocal evidence. The strategy to mitigate this threat was data triangulation. We received surveys from 29 companies, which means that we collected data at different times and locations and from different populations, as can be seen in Table  1 .

Resonance is the extent to which a study’s conclusions make sense to (i.e., resonate with) participants. A key strategy to that end is member checking, so some participants received preliminary results to ensure the correctness of our findings.

Usefulness is the extent to which a study provides actionable recommendations to researchers, practitioners, or educators and the degree to which the results extend our cumulative knowledge. The usefulness of this study is to validate that the vision of IoT companies aligns with the standards generated in the IoT domain. We assume that the industry has also defined the constructors and relationships in these standards.

Transferability shows whether the findings could plausibly apply to other situations. Data were iteratively gathered from 29 companies, a number large enough to build a complete picture of the phenomenon. This multiplicity is what provides the basis for “theoretical generalization”, where the results are extended to cases that have common characteristics and hence for which the findings are relevant [ 58 ]. Furthermore, it is necessary to consider that the theory is substantive (i.e., local to the analyzed surveys). Like any grounded theory study, the result is only applicable to the domain and context being studied and therefore cannot be assumed to be applicable to other contexts or in general.

Dependability shows that the research process is systematic and well documented and can be traced. The public repository contains all the data and procedures used in this research so that other researchers can replicate it.

Conformability assesses whether the findings emerge from the data collected from cases and not preconceptions. As explained in Sect. 4.5.5 , we deliberately omitted any interpretation of the analyzed data, even if this may lead to ambiguities or a vague interpretation of the data. Additionally, as pointed out in Sect. 4.5.4 , to explain some phenomena, we applied abductive reasoning: we assume the premise to be true and seek the most probable explanation.

5.1 Limitations

As with any research methodology, there are limitations to our choice of research methods. The first limitation of our study lies in the number of surveys. The goal of our study was not to generalize a phenomenon observed in a sample to a population: instead, we are generating a theory about a complex phenomenon from a set of observations obtained through theoretical sampling. Grounded Theory does not support statistical generalization. Although the proposed theory appears widely applicable, organizations with different software development cultures in the IoT edge computing domain could have different perspectives.

6 Related work

During the last few years, different work has analyzed the main characteristics of edge computing, its application to specific domains (such as IoT), and the open challenges that should be addressed to increase its adoption by the industry.

Specifically, focused on the application of Grounded Theory to create hypotheses and theories through the analysis of the perception of edge computing by the industry, few resources can be found, as is also stated by [ 13 ]. Some works, such as the one presented by Mengru Tu [ 55 ] studied the intention to embrace IoT in Indian organizations, focused on the logistics and supply chain management area, identifying that benefits and cost perception were important over technology trust. Furthermore, Radanliev P. et al. [ 45 ] use Grounded Theory to identify current gaps in cyber risk standards and policies, defining the design principles of the future cyber risk impact assessment of the IoT. The same authors use Grounded Theory to build a conceptual cascading model for the future integration of cognition in Industry 4.0 [ 44 ] where current and future challenges are identified in the use of Artificial Intelligence in cyber-physical systems.

However, a greater number of works can be found in the literature that analyze these paradigms, through surveys or systematic literature reviews, to characterize edge computing, its benefits and challenges. Some of these works have been analyzed in order to, first, better outline some of the questions presented in this work to the industry; and, second, compare the conclusions obtained from applying Grounded Theory and the conclusions obtained by analyzing the related works. A summary of the analyzed works can be found in   B .

As a summary, the reviewed related works highlight some benefits of edge computing that has also been identified in this work analyzing the responses of the industry, such as the improvement in the quality of service, the performance of the applications, a better user experience, the increase in data privacy, the decrease of network and cloud overhead, and, also, the decrease in the energy consumption. Nevertheless, a notable key benefit for the industry is to better satisfy business needs. This is a crucial benefit for any company that is usually addressed by related work at the second level (Table  13 ). Only [ 39 ], identifies that one of the benefits of edge computing is the ability to create more innovative solutions.This is because academia usually focuses on more technical aspects, which require greater coordination between both worlds to address and provide more business-related benefits. Therefore, the research community needs to invest efforts to show how this paradigm can be applied by the industry to create solutions that better meet the needs of businesses and their customers.

Linked to these benefits, from the companies’ responses, we can also identify a series of challenges that still need to be addressed more deeply, some of them are also highlighted in the related works analyzed. For example (as can be seen in  B ), both companies and academia identify complexity, latency, cost, scalability, security and privacy, and certifications as challenges that need to be further addressed. This alignment between the two worlds will allow us to address these challenges in an agile and successful way and will provide greater usefulness.

However, we have identified some challenges that are relevant to companies and have not been described in the literature so far (Table  12 ), such as: the complexity of the systems using edge computing, the time required to deploy these hyperdistributed systems, the speed in the delivery of the product, how to decrease the time to market applying these solutions, and whether certifications are needed to guarantee the quality of these distributed systems and also those people developing them. As can be seen, these challenges are closely related to the development and maintenance of IoT applications, which is the main concern of companies. On the industry side, companies are the ones that first detect these challenges as relevant because they need to solve them to develop massively systems that apply this paradigm. On the research side, they are currently not core challenges because, although they are important, they are addressed once they are demanded by the industry. This gap between the two worlds may show that the industry’s need of applying this paradigm is closer than expected. Therefore, more effort must be invested to make the application and adoption of edge computing smoother. Furthermore, by solving these challenges, a success similar to that provided by cloud computing can be obtained, in which these challenges were also addressed.

Therefore, although the main benefits and challenges of edge computing are similar in both the research and industry contexts, there are some issues mainly related to the impact in business, such as the improvement of the quality of the applications and the decrease in the time-to-market, which have to be deeply addressed in both contexts to increase the adoption of edge computing.

7 Conclusion

In recent years, the expression “edge computing” has become familiar in the IoT domain. However, not all stakeholders seem to share the same semantics for this expression, leading to confusion in its implementation and application.

The aim of this work has been to develop comprehensive qualitative research that sheds some light on the meaning of edge computing for the industry. The theory developed in this work comprises nine constructs and nine propositions that define the ingredients that, according to the companies interviewed, are central in the edge computing paradigm. From this point of view, the theory satisfies the parsimony criterion (the degree to which a theory is economically constructed with a minimum of concepts and propositions) and is a substantive and analytic theory.

The main contribution of this work is to show, by construction of a theory, that industry and the standard ISO/IEC TR 30164 are mostly aligned. This makes us expect that in the near future the interoperability issues experienced in the edge computing world will vanish or, at least, will be less predominant. It is worth mentioning that the best alignment between industry and the standard is achieved in the discussion of what the functionalities that the edge computing paradigm should support. Our results show unanimity in the expected benefits of the paradigm in terms of resource consumption, security, performance, etc.

There exists also a clear agreement in the sketch of the challenges that the paradigm must address, even though many of them are not appropriate for the paradigm itself but for software engineering. To highlight some of these shared challenges, concerns were detected about how to address the construction and maintenance of complex systems and reliability and efficiency issues. Other challenges are more characteristic of the IoT environment, such as how to increase the computational performance and storage of devices at the edge and how to improve the use of communication networks.

Data Availability

Link to supplementary materials in a long-term repository: https://doi.org/10.5281/zenodo.5034244 . The data of the GT study on EdgeOps were collected from an open-ended questionnaire available at the link https://es.surveymonkey.com/r/PMWD7ZM .

http://msde.etsisi.upm.es/

https://masteriot.etsist.upm.es/?lang=en

Spanish Law 5/2015 indicates that a micro enterprise is one that has less than ten workers and an annual turnover of less than two million euros or a total asset of less than two million euros; a small company is one that has a maximum of 49 workers and a turnover or total assets of less than ten million euros; medium-sized companies are those with less than 250 workers and a turnover of less than fifty million euros or an asset of less than 43 million euros; and large companies are those that exceed these parameters.

This coefficient measures the degree of agreement in the decision to apply different semantic domains, independent of the chosen code.

This coefficient is computed on a specific semantic domain S. It indicates the degree of agreement with which coders identify codes within S.

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Core categories after the selective coding phase

In this appendix, we present Table 10 in which we summarize the collection of cores categories, and well as their inner codes, obtained after the selective coding phase.

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This appendix presents three tables with a summary of the different surveys, systematic review of the literature, and related work that has been analyzed. Table 11 summarizes, for each work, the benefits it provides and the challenges highlighted that must be addressed. Table 12 compares the challenges detected with those highlighted by the related works. Finally, Table 13 identifies the benefits that are also directly defined by the analyzed works.

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REVIEW article

Edge computing application, architecture, and challenges in ubiquitous power internet of things.

• 1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China
• 2 Hunan Institute of Engineering, Xiangtan, China
• 3 Shenzhen Power Supply Bureau Co., Ltd., Shenzhen, China

The Ubiquitous Power Internet of Things (UPIoT) is a concrete manifestation of the Internet of things (IoT) in the power industry, which is a deep integration of the interconnected power network and communication network, realizing full perception of the system status and full business penetration in all links of power production, transmission, and consumption. The introduction of edge computing in UPIoT fully meets the requirements of rapid response, real-time perception, and to some extent, privacy protection. However, there is currently no comprehensive investigation on the application of edge computing technology in UPIoT. First, this paper introduces the development background and construction of UPIoT and its technical architecture. Then the challenges faced by UPIoT in the process of construction are analyzed. Furthermore, the paper elaborates on the functions and features of edge computing, proposes that the support of edge computing technology can solve the challenges of efficient, fast, and secure processing of massive edge data faced by the traditional cloud-based centralized big data processing technology of UPIoT, and analyzes the architecture of the edge computing-assisted UPIoT. For the three typical scenarios of UPIoT, namely power monitoring system, smart energy system and power metering system, the edge computing architecture of the three scenarios are analyzed, and the specific application methods and roles played by edge computing in the three scenarios are also elaborated. Finally, we discuss the challenges of edge computing in UPIoT, in terms of policy challenges, market challenges, and technical challenges, as well as outline the outlooks of the technical challenges.

1 Introduction

The combination of the energy revolution with the digital revolution has led to the development of the fourth industrial revolution. With the application of these new technologies, such as IoT, edge computing, 5G communication, and artificial intelligence (AI) in the power system, the power system is being promoted to become intelligent, digital, and networked. The aim is for these modern technologies to break through the bottleneck of power development and realize 100% renewable electricity. Meanwhile, the development trends of the power grid are to adapt to the diverse needs of “new loads,” improving the flexibility and flexibility of the power grid, and opening up the blue ocean of the digital economy ( state grid corporation of China, 2019a ). In 2019, the state grid corporation of China set the goal of constructing the UPIoT, which integrates the power network and communication networks through various information sensing technologies, intelligent collection technologies, big data technologies, and other modern technologies to connect massive power-related entities, realizing comprehensive intelligent perception, identification, and management of power equipment, information interaction, and data sharing, as well as rapid response to demand.

With the continuous construction and development of UPIoT, a large number of intelligent terminals and devices are accessed, so the computing model with traditional cloud computing as the core is no longer effective in real-time transmission, computation, and storage for the process of the billions or trillions of data that are generated by heterogeneous massive intelligent terminals. Edge computing, a new computing model proposed under the rapid development of the Internet of Things, artificial intelligence, big data, and cloud computing, which is an open platform that uses network, computing, storage, and application core capabilities as a whole on the side close to the physical environment or data source, and the edge computing platform is deployed in the network measurement close to the data source to provide the nearest end service nearby, so as to get faster network service response and meet the basic needs of the industry in terms of real-time services, application intelligence, security, and privacy protection ( Bai et al., 2020 ). Therefore, some services from the original cloud can be allocated to the edge side of the network for processing, so as to meet the real-time requirements of various tasks while ensuring overall system performance ( Sharma and Wang, 2017 ; Fu et al., 2018 ; Zhang et al., 2018 ; Maier and Ebrahimzadeh, 2019 ; Song et al., 2019 ; Xu et al., 2019 ). Especially for supporting ubiquitous IoT, the processing will be done at the local edge computing layer, based on edge computing technology that can localize the computation, analysis, and control to provide a faster response to users without handing over responsibility to the cloud, thus enhancing the processing efficiency and reducing the data processing load in the cloud. Therefore, edge computing technology is naturally similar to UPIoT in terms of agile connectivity, computation, topology, real-time services, data optimization, application intelligence, security, and privacy protection, and can well support the construction of UPIoT.

There is much discussion in the literature of edge computing but very little work has been done towards the edge computing technology that is applied in the UPIoT. For edge computing, some surveys have studied basic characteristics, research challenges, and opportunities of different edge computing paradigms ( Shi et al., 2016 ; Hu et al., 2015 ; Varghese et al., 2016 ; Dustdar et al., 2019 ; Caprolu et al., 2019 ). The concept of edge computing has been extended to the wider IoT. Scholars have discussed the application of edge computing in some IoT fields ( Yu et al., 2017 ). conducted a survey to examine how edge computing can enhance the implementation and the performance of IoT, and compared the performance of the different loT applications that are based on the EC and cloud computing architectures. Alrowaily and Lu (2018) reviewed the concepts, features, security, and application of edge-computing-enabled IoT as well as its security features in the data-driven world. Porambage et al. (2018) surveyed multiaccess edge computing, and they presented a holistic overview of this paradigm in relation to IoT. The integration of multiaccess edge computing into IoT applications and their synergies are also analyzed and discussed. Pan and McElhannon (2018) investigated the key rationale, the efforts, the key enabling technologies, and typical IoT applications benefiting from edge cloud. Omoniwa et al. (2019) presented a survey on EC-based IoT literature in the period from 2008 to 2018, including services, enabling technologies, and open research issues, and briefly displayed how the EC-IoT was applied in real-life cyber-physical systems, such as the intelligent transportation system or smart grid. Qiu et al. (2020) introduced the concept of industrial IoT (IIoT), and presented the research progress and future architecture of the EC-assisted IIoT. As can be seen above, most articles mainly elaborate on the architecture, key technologies, advantages, and challenges of EC-assisted IoT, and there are few implementation plans and deployments for the specific application scenarios of edge computing. However, for the power grids, it simply explains the application and role of edge computing in the smart grid, and it does not specifically cover the various levels of the power generation, transmission, and distribution of power grids, and there is currently no comprehensive investigation on the application of edge computing technology in UPIoT.

This paper focuses on edge computing in UPIoT and combs through many research achievements concerning edge computing in UPIoT, discussing the architecture of edge computing in UPIoT. Then, the application of the three scenarios is explored, namely power monitoring system, smart energy system, and advanced metering infrastructure; meanwhile, the advantages of applying edge computing in the three scenarios of the UPIoT, in terms of data privacy protection, security, and communication time, are analyzed. Finally, we elaborate on the challenges and future directions for the application of edge computing in UPIoT.

2 Autonomous Ubiquitous Internet of Things in Electricity

Promoting the construction of UPIoT is an important initiative to realize the energy Internet, which is a strategic deployment for the development of the world economy and the upgrading of the world energy infrastructure. UPIoT is essentially a kind of Internet of things, a specific expression and application of Ubiquitous Internet of Things in the power industry. (the state grid corporation of China, 2019b ). Around each link of the electric power system, modern information technologies and advanced communication technologies such as mobile Internet and artificial intelligence are fully applied to realize the interconnection of all things and human-computer interaction in each link of the electric power system ( state grid corporation of China, 2020 ). It is a new form of network with a deep integration of traditional industrial technology and IoT technologies, and it is a concrete manifestation of the IoT in the power industry. Overall, it is a smart service system with state awareness, efficient information processing, and convenient and flexible application features. The architecture of UPIoT is shown in Figure 1 .

FIGURE 1 . The architecture of UPIoT.

Like IoT, UPIoT also includes a sensing layer, a network layer, a platform layer (management layer), and an application layer.

2.1 Perception Layer (Terminal Layer)

Smart sensing is the core foundation technology of the perception layer of UPIoT ( Jiang et al., 2019b ). Smart sensing devices are used to collect, monitor, and sense real-time data from end-of-grid operation equipment. The data flow of the power grid operation runs through the whole chain of power generation, power transformation, transmission, distribution, and consumption. For the primary measurement equipment of power system transmission and distribution, part of it is the basic data of the equipment, and the other part is the real-time operation status data of the equipment ( Wang et al., 2019 ). The real-time data is monitored and sensed by the corresponding intelligent monitoring equipment, such as infrared thermometers, monitoring cameras, or inspection robots. For the secondary equipment on the distribution side, such as relay protection devices and electrical energy monitoring equipment, they can collect the system and equipment operation data. The most typical smart sensor of electricity consumption side equipment is the smart meter, which can sense the basic electricity consumption of users in real time and transmit the user’s energy consumption and electric energy trading data to the system platform.

2.2 Network Layer

This is used to achieve efficient and secure data transmission over a wide area between the sensing layer (terminal layer) and the platform layer ( Li Z. S. et al., 2018 ; Wang, 2019 ). In order to meet the access of different types of sensors, it may include different types of network compositions such as mobile communication, limited IoT, and local area network. They have specific communication protocols and specifications and have strong extensibility. With the rapid development of 5G technology in China, 5G communication technology will be gradually applied in electric power IoT. 5G communication has the characteristics of wide wireless coverage, short transmission delay, system security engagement, and high transmission rate, which is the core technology to realize and build electric power IoT.

2.3 Platform Layer

The data transmitted by the network layer is stored and managed through a unified data center, meaning some of the data can also be shared across departments, subjects, and even industries. Using cloud computing technology, deep learning, big data analysis, and other core technologies, it realizes efficient data processing and IoT management. The platform layer is the basis for realizing advanced applications, and is dedicated to improving collaborative computing and real-time response to meet users’ power supply needs and business response.

2.4 Application Layer

The application layer is the core goal of UPIoT, which provides control and decision support for users, electricity sellers, and grid operators. Through the interconnection of power and information, the application layer realizes the production and operation of the grid, operation management, and related energy services and breeds new business models and emerging businesses. These new models include micro-grid operation and management, operation and management of electric vehicles, campus energy management, and system multi-dimensional resource management.

The shape of UPIoT will gradually evolve as key technologies, such as sensor technology, communication technology, and cloud/edge computing technology, are deepened in the smart grid.

3 Architecture of Edge Computing for Ubiquitous Power Internet of Things

3.1 edge computing for ubiquitous power internet of things.

Many new challenges are introduced by the development of the UPIoT. Although current cloud computing has been applied in the UPIoT, it still cannot meet the challenges that we summarize as follows ( Chen et al., 2019 ): 1)low latency requirement, 2)Network bandwidth constraints, 3)Restricted equipment resources, 4)Uninterrupted connection and interaction with the cloud center, and 5)Privacy protection and data security.

In order to overcome these challenges and realize the construction of UPIoT, three key technologies in IoT are important components of its overall architecture: sensor technology, communication technology, and edge computing technology. Among them, edge computing is the important carrier for constructing it, and it is the most important core technology to support the UPIoT in realizing real-time response, short-period data analysis, various types of edge intelligent services, and so on.

Edge computing refers to a new computing model that analyzes and processes a portion of data using the computing, storage, and network resources distributed on the paths between data sources and the cloud computing center ( Shi et al., 2016 ). Edge computing focuses on real-time, short-period data analysis, close to the device side, and it is better able to support local business real-time analysis and intelligent processing. Meanwhile, it has features such as distributed, low latency, high efficiency, and relieves traffic pressure, and it is more efficient and secure compared to simple cloud computing. The main advantages of edge computing applied to the UPIoT are as follows ( Industrial Internet Consortium:Edge Computing Task Group, 2018 ; Du et al., 2021 ):

3.1.1 Improved System Performance

With edge computing, in addition to collecting and transmitting data to the cloud platform, data collected at the edge can be analyzed and processed in milliseconds. For example, the advanced measurement system in the smart grid, where the user electricity consumption data collected by smart meters is uploaded to the edge intelligent fusion terminal, without uploading tens of thousands of data to the cloud platform for processing. The computing software of the edge platform can analyze these data in real-time and only upload the fused or processed data to the cloud center, which greatly reduces the communication broadband, shortens the data transmission time, and improves the overall performance of the system.

3.1.2 Protected Data Security and Privacy

Cloud platform service providers give customers a comprehensive system of centralized data security protection solutions. However, once centralizing stored data get leaked, it will lead to serious consequences. Edge computing migrates computing closer to the device, avoiding the need to upload data to the cloud, which greatly reduces the risk of private data being compromised or corrupted during transmission, and also reduces the security risk of cyber attacks to the cloud center that result in leakage of all stored data stored.

3.1.3 Reduced Operational Costs

Since cloud computing requires uploading data to the cloud center for processing, cloud computing possesses the characteristics of data migration, bandwidth, and latency of cloud computing, which makes it very expensive to use cloud computing, whereas edge computing can significantly reduce operational costs by reducing the amount of data upload, thereby reducing data migration, bandwidth requirements, and latency.

3.2 Architecture of Edge Computing in the Ubiquitous Power Internet of Things

Thousands of power terminal devices and sensing nodes access the smart grid in a variety of ways, and can sense or control the power grid. These nodes are usually organized or self-organized into various clusters to form edge networks ( Cornel–Cristian et al., 2019 ). By deploying edge computing into the UPIoT, it can locally process massive heterogenous data and the acquisition of signals, solving the problem of fast response and centralized service, and reducing cloud pressure and communication overhead. The edge computing reference paradigm in UPIoT is as shown in Figure 2 . The reference paradigm consists of device layer, edge layer, and cloud application layer; its architecture comprehensively describes the characteristics of UPIoT and edge computing. This paper clearly elaborates the inner architecture of the edge computing gateway in the edge layer. In edge computing gateway, which has certain computation resources that can provide a chance to offload part of the workload from the cloud, the edge not only requests service and content from the cloud, but also performs the computing tasks from the cloud. Meanwhile, the gateway can process amounts of data from various terminals, smart devices, and end users of the power terminal layer, and provides a distributed information computing service with large volumes of data and fast responses.

FIGURE 2 . Proposed reference architecture of edge computing architecture and the inner architecture of the edge computing gateway in UPIoT.

Edge can perform computing offloading, data storage, caching, and processing, as well as distribute request and delivery service from cloud to user. Under such circumstances, this mode can satisfy the demand of rapid responses required by devices and users in the smart grids, The internal architecture of the edge computing gateway is shown in Figure 2 ( Liu et al.,2019 ); its software system consists of a host operating system and multiple containers. APPs are running inside the container systems; the cloud server and the edge computing device perform data interaction instructions for uplink and downlink, and the terminal node uploads data to the edge computing device, various software applications run on edge computing devices. By encapsulating the application functions as APPs that are loaded in the container, this can provide support for advanced applications of smart grids such as edge autonomy of smart substations, intelligent online monitoring, rapid request distribution, and service delivery.

The application of edge computing in the IoT has attracted a great deal of research. Some typical IoT scenarios are applied, such as smart transportation, smart healthcare, the smart home, and the smart building. We roughly analyze how to apply edge computing in these four scenarios, and then elaborate on the current related articles on the application of edge computing to smart grids. For example, applying edge computing technology to smart transportation, mobile edge computing (MEC) puts the mobile base stations at the edge of the network, and the mobile base stations are deployed in a decentralized manner, providing servers for applications in base stations close to the edge of the network, allowing data to be processed as close to the vehicle and road sensors as possible, thus reducing the round-trip time for data. The server-side application of mobile edge computing can obtain local messages directly from the vehicle and road sensor applications, identify high-risk data and sensitive information that needs to be transmitted in near real-time through algorithm analysis, and send early warning messages directly to other vehicles in the area to facilitate early decision making by drivers to allow nearby vehicles to avoid hazards, slow down, or change routes ( Li and Liu, 2017 ). It provides relatively real-time driving parameters setting guidelines for different vehicles, and the cloud service platform obtains the historical data from the area edge servers in different regions and applies these to the upper-level applications such as vehicle scheduling across regions, violation monitoring, and traffic map construction and updating. For smart healthcare, with the improvement of living standards, the demand for high quality medical and health care is increasing ( Rahmani et al., 2018 ). Healthcare can also be aided by edge computing. Edge computing applied to smart healthcare systems comprehensively improves global healthcare, and at the same time, brings safer, timely, and effective medical assistance to patients in different regions. In the smart medical system, edge computing and 5G technology are combined in the system to achieve real-time patient information acquisition by IoT devices, ensuring low latency and real-time computing through high-quality network transmission and computing power, realizing remote diagnosis, remote surgery, and emergency rescue, providing flexible and personalized medical services for patients, and meeting the patient’s ability to send data about their health condition without leaving home. For example, MEC can help health advisers to assist their patients, independent of their geographical location. MEC enables smartphones to collect patient physiological information, such as pulse rate and body temperature, from smart sensors and send it to the cloud server. Health advisers who have access to the cloud server can immediately diagnose patients and assist them accordingly ( Stantchev et al., 2015 ). Furthermore, the smart home system is the hot application under the IOT, through computer and communication technologies, the system produces an amount of sensitive data locally, and the data collected by the sensor is transmitted to the decision-making unit, which calculates the appropriate control signals to achieve the predetermined goal. The gateway is applied as the edge that is between the home device and the cloud, through internet services, such as Bluetooth,Wi-Fi, and home LAN, to realize locally controled kinds of smart home devices and remotely operated home devices. Therefore, the edge gateway form the home edge network and the intelligent home service platform, reducing uplink data transmission, service cloud platform load, and responding to user needs with ultra-low latency ( Trimananda et al., 2018 ). Moreover, for the smart building, smart building control systems consist of wireless sensors that are deployed in different parts of buildings. Sensors are responsible for monitoring and controlling building environments, such as temperature, gas level, or humidity. In a smart building environment, sensors installed with edge computing are capable of sharing information and become reactive to any abnormal situation. These sensors can maintain the building atmosphere on the basis of collective information received from other wireless nodes. For example, if humidity is detected in the building, MEC can react and perform actions to increase the air in the building and blow out the moisture. As one of the IoT applications, the smart grid is a cyber physical system covering various smart devices. For instance, Tencent Cloud and Pengmai Energy Technology took edge computing into account and released the overall architecture of energy IoT solution in Cloud Tencent and Energy IoT Pengmai (2018) . ( Okay., 2016 ).proposed a fog computing-based smart grid model, and presented an example scenario, the smart homes, in terms of latency and security, of the advantages of the model. Sun H. Y. et al. (2019) introduced an edge computing technology for power distribution internet of things (PD-loT), and provided the architecture of edge computing for PD-loT, meanwhile, they analyzed the internal and external interaction mechanism of the data center construction under this architecture and the cloud-side collaboration mechanism based on the data center. Furthermore, they have analyzed the application of edge computing in the typical service of power distribution “orderly charging of electric vehicles.” Li B. et al. (2018) discussed the application of edge computing in demand response, and analyzed the application of edge computing in specific scenarios in the field of power supply and demand, such as home energy gateways, non-intrusive load monitoring, and orderly power management. Gong et al. (2018) proposed a packet transport network (PTN) physical architecture model of active distribution network (ADN) based on edge computation, and constructed a cyber physical system (CPS) management and control model of ADN based on edge computation. Kumar et al. (2016) proposed a generalized architecture for data management based on vehicular delay-tolerant network (VDTN) using edge computing for smart gird, and also proposed an energy efficient virtual machine migration utilizing load forecasting. Zahoor et al. (2018) proposed a three-layered framework named cloud–fog-based smart grid, analyzed the edge computing layer close to the consumers’ region that performs effective management of the network resource with low latency, and considered two scenarios for performance evaluation of their cloud–fog-based smart grid model. Liu et al. (2019) presented an architecture of edge computing-driven autonomous ubiquitous IoT in Electricity, which is based on the edge computing architecture and virtual synchronization technology, and introduced the applications that may be deployed in the edge computing gateways. Chen et al. (2019) introduced the services of IoT-based smart grid supported by edge computing, and proposed an architecture introducing edge computing into IoT-based smart grid, as well as presented the three scenarios of the smart grid. Long et al. (2020) discussed the advantages of edge computing technology used in the demand-side management of power consumption in the smart grid. Khan et al. (2020) highlighted the role of edge computing in realizing the vision of smart cities, and reviewed the state-of-the-art literature focusing on edge computing applications in smart cities, including smart transportation, smart health-care, smart grid, and smart farming.

Following an investigation, we found that there are only a few surveys discussing the current status of the applications of edge computing in UPIoT, since it is too new to have attracted too many people’s attention. This paper will analyze the architecture and application of edge computing in three scenarios of power IoT, including distribution network automation monitoring system, smart energy system, and power metering system, as well as analyze the advantages of introducing edge computing in the three scenarios. It provides a significant reference for follow-up researchers, designers, and beginners.

4 Application of Edge Computing in Ubiquitous Power Internet of Things

As shown in Figure 3 ( Chen et al., 2019 ), applying edge computing technology, IoT technology, and 5G communication technology in three major scenarios, improves the performance of the power system and makes the power system more intelligent and automated.

FIGURE 3 . The three scenarios of the IoT-based power system in the edge computing environment.

4.1 Power Monitoring System

The power monitoring system is composed of the control center at all levels, such as substations, power line surveillance, and so on ( Tao et al., 2017 ). The system applies modern control technology, visualization technology, modern communication technology, and Internet of things technology to intelligently monitor power equipment hot spots, power, and environment. It intelligently analyzes data, and realizes comprehensive visualization display and intelligent linkage alarm, at the same time, it effectively assists power equipment informatization, overhaul, and operation. Overall, it serves for smart grid overhaul, operation, and whole life cycle management ( Li, 2019 ). As shown in Figure 4 it mainly describes the two service applications of transmission lines and the intelligent substation in the power monitoring system based on edge computing.

FIGURE 4 . The architecture diagram of edge computing for power distribution system.

4.1.1 Transmission Line Monitoring

On the one hand, in order to realize the intelligent inspection of the transmission line, the unmanned aerial vehicles obtain the image, video, and other data of the surrounding environment of the power transmission through the fixed-focus camera in the pan-tilt. The front-end edge computing detection module recognizes and locates the acquired video stream and the condition of the overhead line. The application of edge computing device nodes in the power line realizes the autonomous, procedural, and standardized collection of image information in the process of the transmission line equipment. Meanwhile, the acquired images and video image data are processed and analyzed locally, reducing invalid information sent back to the cloud platform, and improving the effectiveness of image data information. This method of utilizing the edge computing detection module that reduces the pressure on broadband reduces the load pressure on the cloud platform, and reduces the complexity of subsequent defect diagnosis. Furthermore, the manual participation in inspection operations is reduced, and the efficiency is improved by 5–7 times. On the other hand, it is neccesary to improve the intelligent level of transmission line monitoring and early warning, operation and maintenance, and overhaul. By deploying PMU, FTU, and other sensing units to collect local information, the data types include second-level switch position signals, fault signals, and minute-level signals. Equipment status data, such as electrical information and connector temperature, partial discharge signal, tower status, and channel status, support normalized operations, such as line inspection and troubleshooting. Among them, electrical information, switch status, and local analysis results will be uploaded to the master station layer as remote information, which realize real-time online monitoring and state detection and improve the intelligent level of environmental monitoring and early warning of important transmission channels, as well as realize intelligent operation, maintenance, and overhaul of the power grid.

4.1.2 Intelligent Monitoring of Substation

With the continuous improvement of the IEC 61850 standard and the continuous improvement and deepening of key technologies such as electronic voltage transformers, current transformers, MU, intelligent terminals, and process layers, as well as the continuous construction and deployment of UPIoT, he substation tends to be digital and intelligent ( Tang et al., 2021 ). The system of the substation monitoring system is divided into process layer, bay layer, and station control layer. Substations need to monitor a huge number of secondary equipment such as lines, switch, and circuit breakers, and collect the electrical quantities of the lines and the on-off status of control equipment through the process-level intelligent integrated device of the terminal, then upload them to the GOOSE/SV network. SV messages and GOOSE messages are standard message formats in the IEC61850 communication protocol, they are the line state value and the switch state value, respectively. These messages are transmitted from the process layer to the bay layer, and the bay layer performs line stability control and relay according to the message content. The protection action is performed, and then protection is uploaded and information is relayed to the station control layer in MMS messages, so as to realize the analysis and processing of the monitoring data. Finally, the remote control host at the station control layer transmits it to the dispatch center through the communication protocol in the monitoring system, and waits for the analysis result and task dispatch of the dispatch center. However, both station domain protection and line protection of substations have extremely low delay requirements, and the impact of factors such as message analysis, communication congestion, and network packet loss will bring harm and hidden dangers to the operation of the power grid. By deploying the edge computing platform at the station control layer, the platform process, and storing part of the information on-site by capturing and parsing MMS messages, this would reduce the burden on telecontrol communications while ensuring the quality of application services ( Bai et al., 2020 ). The architecture of edge computing in the integrated automation system of substation as shown in Figure 5 .

FIGURE 5 . The architecture of edge computing in the integrated automation system of substation.

4.2 Smart Energy Systems

The smart energy system is an integrated management system, which is made up of a distributed generator, energy storage devices, flexible loads, and energy conversion devices. The integrated energy management platform coordinates the electrical energy interactions in the power network and uses a microgrid central controller, a distributed power grid connection interface device, and an intelligent control terminal to implement the basic functions of the smart energy system.

The edge computing architecture for the smart energy system is shown in Figure 6 . The architecture consists of three layers: device layer, edge layer, and cloud layer. The cloud layer takes the cloud platform as the core and provides various cloud services. For different scales, the cloud layer can deploy the public cloud, private cloud, or hybrid cloud. The equipment layer consists of various types of power devices, including uncontrolled distributed power sources such as photovoltaic and wind turbines, controlled distributed power sources such as diesel generators and power conversion devices such as inverters, energy storage devices such as electric vehicle charging piles and batteries, and various types of loads. The edge layer is the core of the entire architecture; it consists of edge gateway, edge platform, and edge services. The edge layer provides computing, storage, application deployment, and other functions at the edge side near the data source of the device. The edge gateway is the core device in the edge computing architecture, which collects the operation data of distributed power supplies, loads, power conversion devices, and energy storage devices in real-time, and then uploads them to the edge platform. Under the coordination of the edge platform, each edge gateway executes the control commands derived from the calculation results at the edge side to control the dispatchable power devices ( Xu et al., 2020 ).

FIGURE 6 . The edge computing architecture of the smart energy systems.

We will analyze the typical applications and the related advantages brought by the architecture based on the EC-IoT smart energy systems, including identification of malicious behavior of electricity consumption and Real-time perception of distributed energy power generation status, efficient data processing, and fast reactive voltage response.

4.2.1 Identification of Malicious Behavior of Electricity Consumption

With the massive access of a large number of distributed energy sources and terminals containing power electronic equipment, the terminal equipment in the smart energy system is vulnerable to permission attacks, data storage and encryption attacks, vulnerability threats, and remote control ( Lei et al., 2020 ). These risks will lead to abnormal activities of the terminal’s feedback data, allowing the micro-grid central controller to collect wrong information and then make wrong decision-making activities, causing the local or even the entire system of the micro-grid to collapse ( Komninos et al., 2014 ; Lei et al., 2020 ). In order to monitor malicious behaviors of users online in real time, the smart energy system uses the microgrid central controller, distributed energy grid-connected interface devices, and charging piles, as edge computing modules to form an edge computing platform. For example, the edge computing module establishes the power generation behavior of each device with the characteristics of power and time through the distributed energy generation collected in real time. The edge computing module uses electricity consumption and time as parameters for the user’s electricity consumption behavior in the microgrid, and then establishes the user’s electricity consumption behavior pattern and constructs the database for users. Through the edge module, which can extract power generation and power consumption data anytime and anywhere, then realizes data interaction with the main station, identifies the characteristics of power generation and consumption, and realizes the power balance of smart energy and the identification of malicious behavior ( Mao, 2020 ).

4.2.2 Real-Time Perception of Distributed Energy Power Generation Status, Efficient Data Processing, and Fast Reactive Voltage Response

By installing the edge computing equipment on the grid-connected inverter side of photovoltaic and other power generation equipment, the output electrical quantity and fault information of the inverter in real-time can be monitored and the main data can be uploaded to the integrated energy management system. On the one hand, edge computing devices can collect data from grid-connected inverters, box transformers, and combiner boxes in real time. On the other hand, the architecture of the edge computing module is composed of hardware and software, and has embedded control software. When the grid voltage and frequency collected in real time fluctuate abnormally, there is no need to wait for the control instructions of the cloud platform. The control algorithm adaptively controls the power output of the inverter, and quickly responds to the grid voltage and frequency, so as to realize the comprehensive perception of the state of the power generation unit and efficient data processing ( Sun et al., 2021 ), and quickly support the safe and stable operation of the grid.

4.3 Advanced Metering Infrastructure

Advanced metering infrastructure (AMI) consists of smart meters, data concentrators, data centers, and communication networks. AMI is interconnected with the communication network to achieve two-way communication of power data. In the AMI, the smart meters are uploading their power usage information to the data concentrators through wired and wireless communication in the Neighborhood Area Network (NAN). And then the data center actively requests power data from data concentrators through the wide area network (WAN), or data concentrators pass through the WAN at a preset time interval, and they centrally upload power consumption data to the data center, then the data center distributes electricity price information to users and implements related measures such as load management, demand response, and meter control commands to improve customer service ( Liang et al., 2021 ).

With the rapid development of the smart grid, the data generated by smart meters and other power terminal devices has exploded. Facing the computing demand of massive data, traditional cloud computing solutions face huge challenges in transmission bandwidth, transmission delay, data storage, and real-time response. The introduction of edge computing technology and the introduction of edge computing modules in the data concentrator constitute the AMI edge computing framework, as shown in Figure 7 . The data collected by the terminal is processed locally in the concentrator, and only the calculation results are uploaded to the cloud, thus reducing the network burden, lowering transmission costs, and meeting users’ real-time response needs, etc.

FIGURE 7 . The edge computing architecture of the AMI.

We will present the typical applications and the related advantages brought by the architecture based on the EC-IoT advanced metering infrastructure, including real-time power forecasting and efficient abnormal detection.

4.3.1 Real-Time Power Consumption Prediction

By adding an edge computing module under the current power concentrator, the massive power metering data is passed to the concentrator, and the edge module is an edge device. Based on the computing power of the edge module, the combination of deep learning and machine learning methods is adopted, such as online learning combined with machine learning algorithms (GBDT, XGBOOST, Linear Regression, etc.), which is used to train the time-series consumption data, then realize the real-time prediction of metering data and real-time feedback of consumption bills and the reasonable strategy of the power consumption to users that meet the demand response. Since the data from the massive power terminals are transferred to the edge device, they are no longer transferred to the cloud, and processed directly by the edge device’s own computing resources, which greatly improves the data processing efficiency. In addition, for the new power data generated every moment, the online learning method can be used to quickly complete training and make predictions, which greatly improves the real-time data processing ( Chen et al., 2019 ).

4.3.2 Efficiently Abnormal Detection

The traditional anomaly detection method relies on cloud computing technology to analyze and process all collected data in the data center. A distributed detection method based on edge computing is proposed, which sets up edge node detectors on the edge side of the grid to collect, store, and detect data directly instead of the original central processor. The edge computing-based detection method transforms the traditional centralized detection into a distributed detection method. Combined with the deep learning approach, the anomaly detection model is constructed, and the training process is separated from the edge nodes and placed at the central node to complete. The distributed detection model of the power metering system based on edge computing is shown in Figure 8 . At the edge, the edge node concentrator is responsible for collecting and storing smart meters and related data around them, uploading the processed real-time data and the stored related historical data to the central node, which can perform local processing and detection based on the data they monitor and collect, and sending the trained anomaly detection model down to the edge nodes. In the cloud center, it undertakes the training task of the detection model, and after the model is trained, it is then downlinked to the corresponding edge node. The edge node can realize offline detection, which can realize more efficient and safe monitoring of the power grid and reduce the pressure on the cloud center.

FIGURE 8 . The distributed detection model of the power metering system based on edge computing.

5 Challenges and Opportunity of the Edge Computing in the Ubiquitous Power Internet of Things

The UPIoT seeks to achieve the information-physical-social integration of power energy systems, however, the construction of the UPIoT is still in its infancy at this stage. Although edge computing technology has been gradually applied to various aspects of the power grid, there are still huge challenges to the application of edge computing in the UPIoT. These challenges come partly from the limitations of edge computing technology itself and partly from the potential constraints that exist when edge computing is combined and applied with UPIoT. This section analyzes the market, policy, and technology risks of edge computing in UPIoT applications, and discusses the technical challenges and corresponding outlook for UPIoT based on edge computing.

5.1 Market Challenge

Edge computing enables UPIoT. Constructing UPIoT requires the installation of massive amounts of edge devices, and power operators update traditional solutions to upgrade power infrastructure according to emerging development needs, but it is difficult to quantify its effect on power production, business capacity improvement, and cost reduction, and the corresponding business model is still in the exploration stage. Therefore, the development of the edge computing that is applied in the UPIoT will be faced with market and economic challenges, such as the possibility of investing hundreds of billions of dollars and how to develop operational and business models. Will the return on investment achieve the expected results? And how will the financial crisis be dealt with on the impact of grid planning, investment and operation? All these questions require grid companies, operators, governments, and others to think deeply.

5.2 Policy Challenge

Edge computing realizes the interconnection of power equipment and sensors through the integration of wireless networks, mobile center networks, Internet, and other communication networks. The collection of massive power terminal data must be transmitted to the edge platform through communication, and then the data is processed and analyzed on the edge platform, so it is essential to ensure the security of the edge side network. On the one hand, it is necessary to ensure that data can be reliably and quickly transmitted from the remote end to the platform, and on the other hand, it is necessary to prevent data leakage and resist cyber attacks. Therefore, how to develop new information security protection technologies and systems is a difficult point for edge computing to be applied in the UPIoT, and there is a lack of a unified standard system in the power industry. In Europe and the United States, the development of smart grids is relatively advanced, and the development of security and stability standards is more detailed and comprehensive, For example, the European Telecommunications Standards Institute (ETSI) has defined typical service scenarios and engineering implementation guidelines for edge computing ( European Telecommunications Standards Institute, 2016 ), so the application of edge computing can be promoted in the EU and other countries. However, in China, the security protection of the power secondary system follows the overall protection principle of “security zone, network dedicated, horizontal isolation, and vertical authentication” to ensure the security of the power data network. On the one hand, the standard system related to edge computing is not complete; on the other hand, due to the huge scale of China’s power industry and the relatively conservative policy implementation, it is difficult to apply a new technology immediately. Therefore, in order to realize ubiquitous IOT intelligent sensing, the introduction of edge computing will inevitably lead to network and information security issues, and the relevant specific policies are not yet perfect, so it is difficult to apply and implement edge computing perfectly. Therefore, there are corresponding policy risks for edge computing to be applied to UPIoT, which require in-depth thinking by countries all over the world.

5.3 Technical Challenge

There are many distinctive technical challenges in UPIoT edge computing environments, including management and processing of massive heterogeneous power terminal devices accessing edge side, data offloading and load balancing, edge intelligence, edge network security, data sharing security, and privacy protection.

We summarize these technical challenges in detail and outline potential research directions.

5.3.1 Management and Processing of Massive Heterogeneous Power Terminal Devices Accessing Edge Side

Due to the stage of construction and real-time operation of the power system, a large amount of historical and real-time data, such as control, monitoring, and metering, will be accumulated on the power IoT platform, which constitutes a multi-source heterogeneous data source for the platform layer of the UPIoT. Heterogeneous data sources arise from a variety of power devices, owing to the variety of power devices, the lack of unified access to the edge node standards, and the edge network center node itself which is more difficult to expand. However, moving the location of the edge computing center will bring a lot of delay. Furthermore, in the edge computing network, there are not only static end-devices (e.g., sensors or video cameras), but also dynamic ones such as UAVs and electrical vehicles, making the device management even more challenging. Meanwhile, since different devices have huge differences in hardware configurations and software functions, and the corresponding data computing, storage, and communication capabilities vary, this also offers a challenge ( Qiu et al., 2020 ). Therefore, there is an issue in management of massive heterogeneous power devices accessing the edge nodes, and the edge network must be programmable to support application-specific requirements of edge terminal devices. For this challenge, SDN and NFV technology realize the management and control of multi-source heterogeneous terminal equipment, as well as the scheduling and routing of data flows. SDN and NFV are two of the latest technologies designed to introduce flexibility in network management and orchestration. SDN is mainly characterized by the decoupling of the control plane from the data plane; it provides programmability for network application development and supports the new technology in a unified manner ( Bera et al., 2017 ). SDN and NFV are applied for the edge network. It will make the network more flexible and programmable ( Han et al., 2018 ). Many researchers have carried out certain discussions and research on the application of software-defined network technology in the power Internet of Things, and have achieved relevant results ( Yang et al., 2017 ; Wang et al., 2015 ; Zhong et al., 2021 ), However, the combination of edge computing with SDN, the application of which in the UPIoT is in its infancy, will become a trend and is worth in-depth research in the future. Furthermore, utilizing deep learning techniques on the edge computing platform to assist in edge network control for the large-scale heterogenous power terminal devices is also an area of focus research for studies. At the same time, with the development of 5G networks and its application to the power Internet of Things, in the future, we can study the application of 5G technology to UPIoT-assisted edge computing for edge terminal network management; the edge node management issues will be easy to handle in a 5G network ( Kumareshan and Poongodi, 2016 ). Specifically, 5G core networks can collect status information of various nodes regularly (e.g., node location, resource use, task list, and adjacent nodes), monitor and update node management information and strategies, and optimize other strategies such as data processing strategy and network protection strategy according to the collected information. The combination of edge computing with 5G that adapt to the challenge of management and control in the unified access of massive heterogeneous terminals in the UPIoT, is worth exploring in depth in the future. This is only one side. On the other hand, for a large amount of multi-source heterogeneous data, the computing power is deployed at the edge nodes to deal with heterogeneous data. However, in the actual operation of the UPIoT, on the one hand, the algorithm iteration speed is very fast, and multiple versions of calculation programs cannot be stored locally for a long time. On the other hand, the interface types of terminal devices vary greatly due to the influence of operating environment, communication network, and other factors. There are both wired communication interfaces, such as Ethernet, PLC, RS485/232, and wireless communication interfaces, such as 4G/5G public network. There are both traditional wireless business terminals, such as RTU, PTU, and TTU, and intelligent business terminals. Therefore, it is difficult to process a large amount of multi-source heterogeneous data, which affects the efficiency and performance of edge computing. For this challenge, the technologies of UPIoT microservices, algorithm subscription, and container VM technology are worth studying in depth, which can be carried out to improve the flexibility of algorithm deployment, and then promote agile development and rapid iteration of edge computing in the UPIoT.

5.3.2 Realizing Edge Autonomy to Meet the Business Demand for Real-Time Response

With the construction and development of the UPIoT, the amount of power equipment has increased sharply, and the deployment area is also relatively wide. The on-site installation environment is not only complicated but also diverse, resulting in a sharp increase in the workload of installation, commissioning, management, and maintenance of power devices. Although edge computing is introduced, in current UPIoT systems based on edge computing, edge devices can only perform lightweight computing tasks. To enable edge devices and edge servers to perform more complex tasks with a higher data processing performance and lower latency, edge computing combined with artificial intelligence, big data analysis, and other technologies that are applied in the UPIoT assisted edge computing, which make edge devices and servers intelligent. However, edge equipment resources and computing capabilities are limited, it is difficult to achieve lean management, and there exists a challenge of realizing edge autonomy and Intelligence to meet the business demand for real-time response to improve the quality of service (QoS) and quality of experience (QoE).

Many scholars propose some ideas for coping with challenges from two aspects: the edge device itself and the edge model architecture. In terms of edge computing devices, this could be done by adding AI processor modules or re-designing the intelligent chip to improve the computing power of edge devices. In the design of chip architecture, the aim would be to support the edge computation paradigm and facilitate AI models (e.g., DNNs, CNN, etc) acceleration on the resource-limited IoT devices. Meanwhile, customized AI processors are developed to be better suited to specific edge devices and usage scenarios. For example, developing customized power chips and AI edge computing processors ( Zhao et al., 2021 ) to meet the requirements of massive data processing and calculations. However, there exists a great challenge in developing devices more suited to edge AI and realizing the theoretical complementarity of edge computing and AI ( Qiu et al., 2020 ). At present, in support of the intelligence of the edge computing for the UPIoT, the reconstruct of the edge device is not researched in-depth since some emerging technologies and architectures are under development. On the other hand, artificial intelligence models have been deployed at the edge layer. Although machine learning can be used to enhance the intelligence of edge devices, the high complexity of deep learning in many machine learning methods leads to the relatively high difficulty of the deployment. Due to the computing power of edge devices being generally weak, it is necessary to compress and simplify the model, and optimize the model architecture to adapt to the edge system to improve the processing performance. On the one hand, the technology of model compression for the edge device is worth researching since many approaches, such as model compression, conditional computation, and algorithm synchronization, are proposed to improve the efficiency of training and inference of deep AI models that are utilized in the edge. On the other hand, the architecture of the deep learning model is needed to optimize for the UPIoT assisted edge computing, As we know, the goal of the continuous development and construction of the UPIoT is realizing the transition from “collection + centralized control” to “collection + control + regional autonomy,” and gradually shifting from “vertical closure” to “horizontal openness,” The research of cloud-edge collaboration technology has become a developmental trend of edge computing in the UPIoT. Taking smart substation as an example, it will evolve from three stages as an important infrastructure for the construction of UPIoT: edge interconnection to edge intelligence, and then to edge autonomy. Utilizing the cloud-edge collaborative model for the substation, which remains in the initial stage, needs more efforts to be made, and is an effective way to realize the edge of substation autonomy, and can be the focus point for researchers that need to think and study in the next step.

5.3.3 Edge Network, Edge Node Security Protection, and Data Privacy Protection

Although applying edge computing to the UPIoT can bring the advantages of reduced transmitted data volume on the network, communication delay, computational costs, and enhanced flexibility, a large number of terminals accessed to the edge layer inevitably increase data interfaces that may be used as a springboard to attack edge nodes. On the other hand, edge nodes are close to the edge, and the network protection is weak. In the edge computing scenario, edge computing networks are distributed, scalable, and heterogeneous, the security measures of edge servers are weaker than traditional cloud servers, and they are vulnerable to attacks from malicious nodes in the network. Traditional security protection methods cannot satisfy the protection requirements of edge computing, as the security risks cannot be fully considered at the start of the design. Moreover, the integration of various technologies has also intensified the security threats related to data, networks, and applications. In UPIOT based on edge computing, there are challenges of edge network, edge node security protection, and data privacy protection. In response to this challenge, blockchain is being considered as a disruptive technology by academicians and industries that offers potential solutions to solve the security and privacy issues of edge computing networks and devices ( Kang et al., 2019 ; Frey et al., 2019 ). The incorporation of blockchain and edge computing into a single framework, and then combining the attribute-based access control model, will make it possible to have reliable access and control over the network, storage, and distributed computational resources at the edge. Blockchain technology can also improve the security of the EC-assisted IoT paradigm as it permits only trusted IoT devices/nodes to interact with each other. On the one hand, blockchain-based trusted data management schemes (e.g., lightweight consortium blockchain) for cooperative authentication, authorization, and privacy preserving could be developed ( Gai et al., 2019 ; Mao et al., 2020 ; Wang et al., 2020 ), meanwhile, utilizing blockchain to form a security mechanisms for edge nodes/devices could ensure the security and credibility of edge nodes in the UPIoT. These two proposed methods can not only prevent edge nodes from being attacked and lead to data privacy of the problem of leakage, but also ensure the integrity and security of the data sharing using the external network; therefore, it ensures the credibility of regional terminal computing tasks in the edge layer, and then returns the correct calculation results to the cloud and terminal users safely and reliably. Overall, these are potential research directions for studies, and need more research and developments. On the other hand, the combination of blockchain and machine learning methods (such as federated learning, deep learning, reinforcement learning, and deep reinforcement learning) to enhance the access control, secure storage, and privacy -preserving data of EC-assisted UPIoT, can be adopted to detect abnormal behavior, such as energy theft with energy privacy protection in the smart gird ( Yao et al., 2019 ), which is an emerging field and a promising direction for research.

5.3.4 Edge Computation Offloading and Load Balancing Realize Demand Response

UPIoT is a promising solution to meet the increasing electricity demand of modern cities, while challenges face the real-time processing and analysis of huge data collected by the power terminal devices due to the limited computing capability of the devices and long distance transmission from the cloud center. Edge computing enables power terminal tasks to be offloaded on the edge side for in-situ processing. It reduces communication delays and energy consumption. However, the power consumption, computing power, and storage space of edge-layer servers are also limited. Computation offloading and load balancing are great challenges in UPIoT systems based on edge computing.

For the challenge of the data offloading, scholars mostly combine artificial intelligence technologies ( Lin et al., 2019 ; Sun W. et al., 2019 ) (such as deep reinforcement learning ( Dinh et al., 2018 ; He et al., 2018 ; Dai et al., 2019 ; Luo et al., 2019 ; Min et al., 2019 ; Zhang et al., 2019a ; Zhang et al., 2019b ), such as markov chain decision-making, game theory, Lyapunov optimization, machine learning and so on) to improve the performance of computing offloading schemes. However, according to the characteristics of UPIoT, to find the optimal balance between energy consumption, delay, amount of data, bandwidth, it is necessary to design the computation offloading strategy according to the data volume, task type, and equipment capabilities ( Jiang et al., 2019a ; Pan et al., 2020 ), which is worthy of in-depth consideration, and is the outline for future studies by researchers at this stage. Furthermore, another challenge is considering how to allocate resources reasonably after making the data offloading decision, that is, the problem of where the resources are distributed. In other words, there is a new problem in that the data offloading scheme may lead to the overload of some edge devices, then creating another challenge of load balancing. Load balancing based on specific characteristics and scenarios of the UPIoT combining new technologies is a significant research direction. Considering the scale and frequency of scheduling are significantly larger, it is necessary to improve the existing load balancing algorithms to adapt the characters of the UPIoT edge system. A typical method depends on the NFV and SDN integrated edge cloud platform to orchestrate the resources to fulfill the offloaded tasks from the battery-constrained edge terminal devices. In UPIoT based on edge computing, using the task data and loading data from the equipment and edge servers, combined hierarchically with AI, a load balancing service can set up the load balancing scheme based on a machine learning model for each layer. In addition, SDN can be utilized to conduct load balancing scheduling from the global perspective of the edge network. To minimize the complexity of scheduling and routing, emerging SDN technology will have a significant impact on the routing scheme and communication mode of edge network, and brings more comprehensive and in-depth routing schemes for edge computing in UPIoT ( Kaur et al., 2018 ; Li X. et al., 2018 ; Nayak et al., 2018 ; Al-Hubaishi et al., 2019 ). At present, some scholars propose to use SDN to establish a grid edge computing model, with minimum delay as the goal orientation, and use deep reinforcement learning to reasonably schedule and allocate computing resources ( Shang et al., 2021 ). However, there is very little research on resource allocation strategies that target how to balance time delay and energy consumption, which is a direction worthy of research. On the other hand, Machine learning algorithms based on resource allocation in edge-cloud architecture, edge-edge architecture, could also solve the problem of load balancing. For example, based on the edge-cloud architecture, the computing tasks of the terminal cannot be completely offloaded to the edge side for execution, so some tasks are offloaded to the remote cloud server for calculation, and the result will be first returned to the edge server, and finally back to the terminal device. If a client’s requirement is more critical, it will be handled by the cloud; otherwise, servicing is done by the edge. However, the reality of how edge computing and cloud computing can work together efficiently and seamlessly is a significant research direction ( Li et al., 2020 ). When considering the computation offloading in edge computing, it is necessary to consider the gaming and cooperation between the edge and the cloud for task scheduling and collaboration.

6 Conclusion

Edge computing integrates network, computing, and storage on the edge of the network. The introduction of edge computing can solve the problems of cloud computing architecture facing the UPIoT, which is unable to handle massive heterogeneous data, communication delays, high computing pressure, data privacy leakage, and difficulty in satisfying user demand response and other issues. First, this article introduces the edge computing technology and the framework of the UPIoT, and gives the architecture of the combination of edge computing and the UPIoT and the internal architecture of the edge computing layer. Moreover, one of the contributions of this paper is to analyze the technical application of edge computing in the three power Internet of Things scenarios: power monitoring system, smart energy system, and power metering system. It also gives the architecture of the edge computing in the three scenarios. Furthermore, the major contribution is putting forward the policy challenges, market challenges, and technical challenges of the application of edge computing in UPIoT, meanwhile, the technical challenges and outlooks in four major areas are analyzed in detail. This paper aims to obtain more attention from other researchers in edge computing in the UPIoT, and make power industry development more rapid and convenient.

Author Contributions

DL conceived the overall structure and framework of the article. HL conceived the outline of the manuscript. QZ and ZZ wrote the manuscript and generated the figures. XZ and ML helped perform the manuscript with constructive discussions. All authors contributed to the article and approved the submitted version.

This work was supported in part by the National Natural Science Foundation of China (Grant No. 52177068), the Research Project in Hunan Province Education Department (Grant No. 21C0577), Postgraduate Research and Innovation Project of Hunan Province, China (Grant No. CX20210791) and in part by Key Laboratory of Renewable Energy Electric-Technology of Hunan Province (Changsha University of Science and Technology).

Conflict of Interest

ML was employed by Shenzhen Power Supply Bureau Co., Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: power internet of things, UPIoT, edge computing, edge computing applications, edge computing architecture, smart grid

Citation: Liu D, Liang H, Zeng X, Zhang Q, Zhang Z and Li M (2022) Edge Computing Application, Architecture, and Challenges in Ubiquitous Power Internet of Things. Front. Energy Res. 10:850252. doi: 10.3389/fenrg.2022.850252

Received: 07 January 2022; Accepted: 27 January 2022; Published: 22 February 2022.

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Copyright © 2022 Liu, Liang, Zeng, Zhang, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dongqi Liu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Title: the benefits of edge computing in healthcare, smart cities, and iot.

Abstract: Recent advancements in technology now allow for the generation of massive quantities of data. There is a growing need to transmit this data faster and more securely such that it cannot be accessed by malicious individuals. Edge computing has emerged in previous research as a method capable of improving data transmission times and security before the data ends up in the cloud. Edge computing has an impressive transmission speed based on fifth generation (5G) communication which transmits data with low latency and high bandwidth. While edge computing is sufficient to extract important features from the raw data to prevent large amounts of data requiring excessive bandwidth to be transmitted, cloud computing is used for the computational processes required for developing algorithms and modeling the data. Edge computing also improves the quality of the user experience by saving time and integrating quality of life (QoL) features. QoL features are important for the healthcare sector by helping to provide real-time feedback of data produced by healthcare devices back to patients for a faster recovery. Edge computing has better energy efficiency, can reduce the electricity cost, and in turn help people reduce their living expenses. This paper will take a detailed look into edge computing applications around Internet of Things (IoT) devices, smart city infrastructure, and benefits to healthcare.

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