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We are pleased to announce that the following Research Article published in EURASIP Journal on Wireless Communications and Networking has been awarded the 2024 EURASIP best paper award!

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach Authors : Zhao Chen and Xiaodong Wang

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Current near-field communication (NFC)-based commercial sensors (technology readiness level (TRL) ≥7) and research sensor prototypes (TRL <6). Sensors are categorized into four classes according to the type of application. Health-care sensors: wearable (adhered onto skin), implantable (inserted in the body) and point-of care (near-patient monitoring) sensors; and food quality (attached to packaging) sensors.

Current NFC-based sensing technologies

Only few NFC-based sensors are commercially available today, with a technology readiness level (TRL) of 7 or above (TRL is a method for estimating the maturity of a technology, with 9 being highest). Most of these NFC-based sensors focus on health, with only few devices truly exploiting both wireless power and communication capabilities. Examples include the intraocular pressure sensor, Eyemate by IOP GmbH, and the wearable glucose monitoring system, FreeStyle Libre by Abbott (powered using a battery rather than NFC). In the food industry, NFC-based sensors have been commercially piloted by Kraft Heinz Company for tamper-proofing and marketing; HZPC, a seed potato supplier, is trialing an NFC-based temperature sensor.

NFC-based sensing technologies are increasingly explored by the academic community (TRL ≤6), with a major focus on applications in health care and food quality monitoring. In health care, NFC-based ‘tattoo-like’ wearable disposable sensors are perhaps the most developed technology. These sensors use a thin and flexible polymer substrate (polydimethylsiloxane (PDMS) is standard) that is adhered to the skin in order to provide non-invasive and mostly biophysical measurements (for example, electrocardiogram (ECG), skin temperature and haemodynamic parameters), typically using a smartphone as the reader 1 , 2 . Non-invasive biochemical sensing of biofluids, such as analytes in sweat, is also explored 3 , and working prototypes have been tested on humans 1 , 2 . Commercial translation is currently limited, however, by poor durability, noise (for example, electrical noise) and high manufacturing costs (PDMS, or more broadly, silicones, are not a standard material for high-volume manufacturing of flexible devices).

A completely disposable NFC-based electrochemical point-of-care immunosensor has also been developed for detecting viruses such as the hepatitis B virus, providing a viable alternative to current disposable electrochemical point-of-care diagnostics, which require a dedicated reader (for example, glucose test strips) 4 .

In addition, implantable NFC-based sensors have been developed to address longstanding issues associated with batteries (that is, toxicity, bulkiness and battery recharging or removal) 5 , 6 . Clinical translation is currently hindered by low biocompatibility, low robustness (operational lifetime and mechanical integrity), invasiveness and lengthy regulatory processes.

Disposable NFC-based electrical gas sensors show potential for monitoring food quality. These sensors can be included in food packaging to monitor food freshness and safety by measuring gases released by microbial spoilage 7 , 8 . This technology could replace the static (and often confusing) ‘best-before’ dates, providing dynamic information about the chemical or microbial state of a product, which will help reduce food waste and foodborne illnesses. The price constraints of a few cents remain a major challenge, however.

Future requirements for materials

Whether used inside or on the surface of the body, medical sensors are subject to deformation (that is, bending, twisting and stretching), which can compromise the integrity of NFC-based sensors. Creases in the traces of the coil antenna can affect performance and impact energy and data transmission; a tear in the trace would halt operation altogether. Metals, such as GalnSn and EGaln alloys, are liquid at room temperature and offer high thermal and electrical conductivity as well as low toxicity. Such liquid metals can be inserted into soft polymer microchannels (<100 µm in height) to create traces for NFC-tags, which can be stretched, squeezed and folded with a minimum bending radius of 0.15 mm 9 . Off-the-shelf rigid electronic and sensing elements remain susceptible to fracture, however, leading to device failure. Flexible and stretchable electronic (computational) components are, therefore, required to realize truly robust NFC-based sensors.

The cost of disposable NFC-based sensors needs to asymptotically approach zero for low-margin, high-volume applications, for example, food quality sensors. Flexible synthetic (PET) or natural (cellulose) substrates with metal traces can be inexpensively manufactured as part of packaging. However, sensing elements and electronics remain the costliest factor. This can be resolved by exploiting the intrinsic properties of the substrate or conductive traces for sensing 8 . Additionally, films that form part of the packaging can be used as protective layers for sensors. Some films are hydrophobic and gas permeable — essential characteristics for the barrier layers of gas sensors used for monitoring food quality. Most NFC-based sensors depend on microcontroller-based multicomponent designs; the versatility (and underused capabilities) of these designs, however, comes at a premium. First examples of application-specific integrated circuits (ASICs) have already been developed for low-cost NFC-based sensors (for example, potentiostat), but a large gap remains between available and required technologies, owing to high initial investments needed for the development of ASICs 4 .

NFC-based sensors are normally designed as 2D devices with a planar coil. The coil must generate a certain inductance to optimally function at 13.56 MHz, which restricts the minimum size of the coil (to a few centimeters in diameter). For further miniaturization, multi-layer coils can be implemented to boost the efficiency of exchanged power. Coil antennas as small as 10 mm (double layer) have been reported 1 , providing a suitable length-scale for most applications. Implantable sensors at this size, however, require invasive surgery for insertion and removal, increasing the risk of infection and incurring high costs. Thus, additional miniaturization techniques must be developed for implantable NFC-based sensors.

To overcome the serious risks (for example, infection) associated with the surgical removal of implants, NFC-based bioresorbable sensors have been developed that resorb into non-toxic byproducts in the body 5 . Molybdenum and magnesium are often used as bioresorbable antennas and conductors; co-polymers, such as poly-lactic acid (PLA) and poly(lactic-co-glycolic acid) (PLGA), can be used as substrate and encapsulation materials. For encapsulation, an additional layer of the co-polymer is typically stacked on top of the electronics and adhered to the substrate to seal the device. However, weak interfacial adhesion between layers often results in premature degradation or limited operational lifetime (<6 days). A recently developed long-lived (>30 days) bioresorbable polyurethane achieves greater stability owing to its ability to form covalent bonds with itself, providing stronger adhesion 10 .

Most bioresorbable NFC-based sensors are only partially resorbable because not every component or material in the device is able to resorb (including digital NFC chips). NFC and computation require digital logic; silicon, the dominant semiconductor used in digital electronics, is a bioresorbable material, and thus, completely bioresorbable silicon chips may enable fully bioresorbable NFC-based sensors in the future.

Environmental considerations

Disposable NFC-based sensors are made with a combination of materials, which makes them difficult to recycle. For high-volume disposable applications, sensors should ideally be produced from environmentally friendly, non-toxic, biodegradable and sustainable materials, such as cellulose paper, copper and silicon or ceramics. When non-biodegradable materials must be used, designs that maximize overall recyclability should be selected. For example, delamination of sensors should be considered in cases where non-recyclable disposable NFC-based sensors are attached to (single plastic) recyclable packaging. Given that multilayer laminates, which are included in most food packaging, cannot be easily recycled, NFC-based sensors may be attached or embedded in these films for specialized recycling (or incineration).

The road ahead

NFC-based sensing is still in its infancy. The potential of NFC to enable entirely new sensing concepts will likely play an important role in the future of the ‘connected’ technological revolution. Challenges remain to be addressed, however, particularly in terms of materials and manufacturing, to turn laboratory prototypes into commercial products. Interestingly, the current COVID-19 pandemic stresses the need for contactless interactions beyond payment systems (from zero-touch shopping to wireless diagnostic tests), the effect of which will likely accelerate NFC-based sensing technologies. Moreover, because data security is at the core of NFC, NFC-based sensors may be paired with high-security technologies, such as blockchain, to create trusted, automated and connected sensing systems. This combination would improve integrity, accessibility and speed of operations within food and health-care systems.

Kim, J. et al. Miniaturized battery-free wireless systems for wearable pulse oximetry. Adv. Funct. Mater. 27 , 1604373 (2017).

Article   Google Scholar  

Lee, S. P. et al. Highly flexible, wearable, and disposable cardiac biosensors for remote and ambulatory monitoring. npj Digital Med. 1 , 2 (2018).

Xu, G. et al. Smartphone-based battery-free and flexible electrochemical patch for calcium and chloride ions detections in biofluids. Sens. Actuators B. Chem . 297 , (2019).

Teengam, P. et al. NFC-enabling smartphone-based portable amperometric immunosensor for hepatitis B virus detection. Sens. Actuators B. Chem. 326 , 128825 (2021).

Article   CAS   Google Scholar  

Kang, S. K. et al. Bioresorbable silicon electronic sensors for the brain. Nature 530 , 71–76 (2016).

Zhang, H. et al. Wireless, battery-free optoelectronic systems as subdermal implants for local tissue oximetry. Sci. Adv. 5 , eaaw0873 (2019).

Escobedo, P., Bhattacharjee, M., Nikbakhtnasrabadi, F. & Dahiya, R. Flexible strain sensor with NFC tag for food packaging. 2020 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) (2020).

Barandun, G. et al. Cellulose fibers enable near-zero-cost electrical sensing of water-soluble gases. ACS Sens. 4 , 1662–1669 (2019).

Teng, L. et al. Liquid metal-based transient circuits for flexible and recyclable electronics. Adv. Funct. Mater. 29 , 1808739 (2019).

Choi, Y. S. et al. Stretchable, dynamic covalent polymers for soft, long-lived bioresorbable electronic stimulators designed to facilitate neuromuscular regeneration. Nat. Commun. 11 , 5990 (2020).

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Acknowledgements

S.O. acknowledges the Imperial President’s PhD Scholarships. F.G. and H.S.L. thank the LISS DTP (2453729) for the financial support. F.G. also thanks Imperial Centre for Processable Electronics and Agri-Futures Lab.

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Selin Olenik, Hong Seok Lee & Firat Güder

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S.O. and F.G. wrote and edited the manuscript. H.S.L. edited the manuscript and conceptualized the figure.

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Correspondence to Firat Güder .

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Olenik, S., Lee, H.S. & Güder, F. The future of near-field communication-based wireless sensing. Nat Rev Mater 6 , 286–288 (2021). https://doi.org/10.1038/s41578-021-00299-8

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Published : 02 March 2021

Issue Date : April 2021

DOI : https://doi.org/10.1038/s41578-021-00299-8

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Special issue on Wireless communication systems in beyond 5G era

The​​​ ​ me 

During the development and deployment of 5G mobile cellular systems, a number of new technological concepts, advances and paradigm shifts have emerged, altering the perspective of research community on how one should design wireless communication systems in the future. The proliferation of machine learning and artificial intelligence tools and technologies, while having limited effect on 5G, are already demonstrating their imminent future impact on the design of communication systems across the layers of the traditional communication protocol architecture. Most notably, these technologies further accelerate the trends of cognition and self-organization, ranging from the device spectrum access level, across algorithms governing physical and medium access layer operation, all the way to the level of network organization and resource allocation. ​​​​ In addition, advent of new materials, combined with their controllability and programmability, transforms the propagation environment from a passive entity into an active communication system ingredient, especially in the domain of high-frequency (e.g., THz-domain) wireless communications based on directed and pencil-beam signal propagation. In another development, the ever-increasing densification of cellular infrastructure is gradually escaping the Earth surface and we are witnessing introduction of the third, aerial dimension where dense deployments will firstly emerge at a very low-height level using Unmanned Aerial Vehicles (UAVs), such as drones, and Low-Earth Orbit (LEO)-level using micro-satellite constellations, creating novel challenges in 3D network design and optimization.  Next, going to the domain of miniaturization and wireless sensor platforms, progress from on-body to in-body sensors is offering not only further prospects of creating novel human-machine interfaces that go beyond the existing trends of virtual and augmented reality, but promise future impact on biomedical research, diagnostics, and therapeutics.  The question of energy efficient communication technologies operating at network-wide scales to address raising global energy consumption challenges and climate change concerns and, at the opposite end of the energy consumption spectrum, wireless power transfer technologies for the deployment of self-sustainable and battery-less IoT sensors, are expected to create significant impact on future wireless system design.  Finally, overlaid on the potentials of the technology evolution as described above, lies the key question: What are the future services and applications for which we need to design novel beyond 5G wireless communication systems?  ​This special issue is dedicated to exploration of future and evolving technologies that are likely to have significant impact on the design of wireless communication systems in the beyond 5G era.​

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Beyond 5G, 6G, wireless communication systems, machine learning and artificial intelligence (AI)

• Cognitive and dynamic spectrum access in beyond 5G systems
• Machine learning and AI for wireless communications system design beyond 5G
• Wireless communications with intelligent reflecting surfaces (IRS)
• THz wireless communications ​
• Internet of Things and edge AI integration
• 3D networks of terrestrial, airborne and satellite communication systems
• Large scale wireless powered networks and backscatter communications
• Network softwarization and virtualization in beyond 5G era
• Communication systems and networks at nanometer-scales
• Future carbon-neutral wireless communication networks
• Applications and services driving beyond 5G communication system development​​

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Open Access

Peer-reviewed

Research Article

Analysis of symbiotic backscatter empowered wireless sensors network with short-packet communications

Roles Conceptualization, Writing – original draft

Affiliation Wireless Communications Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Roles Formal analysis, Writing – original draft

Affiliation Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

Roles Formal analysis, Methodology

Affiliation Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam

Roles Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Software and Communications Engineering, Hongik University, Sejong, South Korea

• Quang Vinh Do, 
• Bui Vu Minh, 
• Quang-Sang Nguyen, 
• Byung-seo Kim

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0307366
• Peer Review
• Reader Comments

Recent progress studies in light of wireless communication systems mainly centred around two focuses: zero-energy consumption and ultra-reliable and low-latency communication (URLLC). Among various cutting-edge areas, exploiting ambient backscatter communication (Backcom) has recently been devised as one of the foremost solutions for achieving zero energy consumption through the viability of ambient radio frequency. Meanwhile, using short-packet communication (SPC) is the cheapest way to reach the goal of URLLCs. Upon these benefits, we investigate the feasibility of Backcom and SPC for symbiotic wireless sensor networks by analyzing the system performance. Specifically, we provide a highly approximated mathematical framework for evaluating the block-error rate (BLER) performance, followed by some useful asymptotic results. These results provide insights into the level of diversity and coding gain, as well as how packet design impacts BLER performance. Numerical results confirm the efficacy of the developed framework and the correctness of key insights gleaned from the asymptotic analyses.

Citation: Do QV, Minh BV, Nguyen Q-S, Kim B-s (2024) Analysis of symbiotic backscatter empowered wireless sensors network with short-packet communications. PLoS ONE 19(8): e0307366. https://doi.org/10.1371/journal.pone.0307366

Editor: Kamarul Ariffin Noordin, Universiti Malaya, MALAYSIA

Received: March 24, 2024; Accepted: July 4, 2024; Published: August 26, 2024

Copyright: © 2024 Do et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: We confirm that “All relevant data are within the manuscript and its Supporting information files.” We have also provided our simulation code as Supporting information Files.

Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1003549).

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

With the widespread adoption of the Internet-of-Things (IoT), next-generation wireless networks are seeing an influx of emerging IoT applications and services [ 1 ]. These applications and services are not limited to interactions between a specific system, such as wireless sensor networks (WSNs), cellular networks, or vehicle networks, but also extend to new categories such as telemetry, healthcare, smart grids, digital twins, and the metaverse [ 2 ]. In which, artificially intelligent technology plays a core role in controlling, collaborating and coordinating network components [ 3 ]. Along with the benefits brought by IoT context, it also renders several challenges to the development of physical wireless systems [ 4 ]. For instance, how to accommodate the increasing demand for connected devices when the current spectrum is full, prolong the activities of IoT devices with limited power resources, and provide ultra-reliable and low-latency wireless communication (URLLC).

Amidst the shortage of available radio frequencies, several efficient strategies have been proposed to tackle this challenge. The first aim is to line on full-duplex communication to increase spectrum efficiency [ 5 ] but requires refined successive interference cancellation (SIC) approaches to move residual interference it produces [ 6 , 7 ]. Another strategy is to take advantage of multiple access technologies, such as non-orthogonal multiple access [ 8 ] and rate-splitting multiple access [ 9 ], which enable multiple users to communicate simultaneously using the same time and frequency resource block. However, exploiting such multiple access is tedious because it is only suitable for a single network. As a result, recent progress on multiple access typically combines it with two potential types of cognitive radio (CR) paradigms [ 10 , 11 ], overlay and underlay. While underlay allows the secondary network to coexist with the primary network subject to power-tolerant constraints, overlay networks operate under free interference [ 12 ]. However, with the disadvantage characteristics by themselves, the realization of CR solutions also becomes controvertible [ 13 , 14 ].

In the wake of energy constraints, energy harvesting (EH) solutions have emerged as an alternative that allows IoT devices to charge their batteries without human intervention [ 15 ]. Compared to charging energy from surrounding natural sources such as wind or sun, radio-frequency (RF) EH is voted as the most cost-effective solution, with two representations of wireless-power transfer communication network and simultaneously wireless information power transfer [ 16 ]. According to many reports in the literature [ 17 – 22 ], the nature of active communication enabled by RF signals consumes relatively high power, and this might not be favourable to large-scale IoT deployments in the long term. Thus, it raises the question of finding new alternatives with sustainable RF-EH capabilities and low power consumption.

Driven by the two necessities above, symbiotic communication, a new paradigm shift for passive IoT, has recently emerged as the ultimate solution to tackle the issues of spectrum scarcity and low-power consumption [ 23 ]. Symbiotic paradigm is a revolutionary concept that expertly blends the strengths of two existing paradigms [ 24 ], ambient backscatter communication (Backcom) and CR. In this paradigm, the backscatter device passively modifies the received signal from the primary transmitter with its information before sending it back to the secondary receiver [ 25 ], effectively changing its load impedance instead of using dedicated RF components. This allows Backcom to act similarly to CR paradigms while consuming zero energy. This functionality helps a symbiotic paradigm to achieve properties of mutualism, commensalism or parasitism [ 26 ]. Due to this prominent feature, the research on Backcom with symbiotic mediums has recently attracted significant momentum from both industrial and academia. For example, a full-duplex Backcom solution was proposed to symbiotic radio (SR) system [ 27 ]. In [ 28 ], three practical cooperative transmission schemes was proposed for symbiotic radio systems. The work in [ 29 ] provided a thorough and authoritative review of the systematic view for SR, along with critical discussions to enhance the backscattering link, achieve highly reliable communications, and effectively utilize reconfigurable intelligent surfaces. In [ 30 ], a novel beamforming design was proposed to multiple-input-multiple-output SR backscatter system. Meanwhile, the work in [ 31 ] studied SR communication system in the presence of multiuser multi- backscatter-device. In [ 32 ], two SR schemes were designed for a pair of backscatter devices, is that, opportunistic commensal and opportunistic parasitic. In [ 33 ], a symbiotic localization and Backcom architecture was developed for IoT localize target objects to achieve two mutual benefits: sensing and communication stage. In [ 34 ], an investigation of Backcom was put forward in symbiotic cell-free massive multiple-input multiple-output systems. Meanwhile, an innovative solutions for enhancing the security of low-power IoT devices using ambient backscatter communication was introduced in [ 35 ], with a strong focus on the balance between energy efficiency and security. In a very recent time, the work in [ 36 ] presented advances in enhancing the robustness of wireless communication systems against jamming attacks by designing a novel beamforming technique that utilizes the concept of symbiotic radio to effectively use the null space of interference, thereby enhancing safeguarding data transmission significantly.

On the other hand, to deal with stringent URLLC conditions, where transmission latency is expected to be less than 1 ms while reliability is from 99.9% to 99.9999%, recent efforts propose to rethink the design of packet size [ 37 ]. Specifically, reducing packet size to improve the physical layer transmission latency; however, this action results in a higher error rate transmission. In this case, there is no way to use a finite blocklength message coding scheme to boost reliable communication. Based on Polyanskiy’s novel infinite block length theory, published in 2010 [ 38 ], the research on short-packet communication (SPC) has recently emerged as a vital solution and is receiving considerable interest from research communities [ 39 – 42 ]. In that, block-error rate (BLER) is devised to be the key metric instead of using outage probability or ergodic Shannon capacity for the performance evaluation.

Towards a green IoT network for the future, the interplay between symbiotic Backcom and SPC becomes the pivotal direction. In the past, several works investigated the benefits brought by SPC with conventional Backcom systems (backscatter devices are deployed for enhancing communication coverage only), such as resource allocation [ 43 ], energy efficiency [ 44 ], and error performance for finite backscatter channels [ 45 ]. However, to the best of authors’ knowledge, the research on symbiotic Backcom systems with SPC remains unexplored in the literature. Therefore, this inspires us to investigate the feasibility of SPC in symbiotic Backcom systems. In particular, the main focus of this work is on the performance of symbiotic Backcom-empowered WSNs with SPC, where the secondary backscatter transmitter is parasitic from the primary network. Overall, the main contribution of this article can be outlined as follows:

• Towards future green URLLC use cases, this article studies the performance of symbiotic backscatter-empowered WSNs, where a passive backscatter device with energy constraints in the secondary networks exploits ambient RF signals generated by the primary transmitter for the primary receiver as a green power source to be able to communicate with the secondary IoT receiver. To reject interference impacted by a primary transmitter’s RF signals, SIC enables the IoT receiver to decode its signal from a passive backscatter device.
• How much diversity and coding gains does the considered system achieve when compared to a system using uncoded transmission?
• How do the packet designs affect the BLER performance?
• To validate our developed mathematical framework, we provide some extensive numerical results based on Monte-Carlo simulations method. It is interesting to show that this framework accurately predicts the actual result with very minor errors, even with a series of approximation approaches used. Besides, it also confirms the performance trend findings of the reflection coefficient designed at the backscatter device, the packet construction involving packet length and number of information bits, as well as the transmit power of the primary transmitter. Furthermore, we through numerical results have that when boosting the reflection coefficient exceeds 2.5 (unit), the BLER performance of the secondary IoT receiver converges to saturation.

The remainder of this article is covered as follows. Section 2 describes the system model, followed by the average BLER analysis in Section 3. Next, Section 5 provides some numerical results before concluding the article in Section 5.

2 System model

Let us consider a symbiotic backscatter communication system as shown in Fig 1 , where the cellular network, called the primary network, coexists with an IoT sensor network, called the secondary network. In this setup, a backscatter device (named by BD) exploits the available RF signal when carrying a symbol x ( t ) sent from the primary transmitter (denoted by PT) to the primary receiver (called PR) to convey its symbol information c ( t ) to the secondary IoT sensor receiver (i.e., IR), with t being the time t . In which, the packet information sent by BD has data amount N IR bits with packet length L IR (or the equivalent terminologies: channel use or blocklength), while that of PT includes data amount N PR bits with packet length L PR . Due to the presence of the multiplicative fading phenomenon and long-distance communication, no interference occurs from BD to the signal of PR [ 27 , 29 , 33 ]. Meanwhile, there always exists interference from PT to IR, which therefore requires the adoption of SIC approach at IR to subtract x from the received signal before detecting c ( t ). In this investigation, all wireless channels are assumed to follow quasi-static Rayleigh block fadings, which means that channels vary very small or even with static. Thus, it is reasonable to consider the availability of global channel state information at the terminals via statistical channel measurement methods [ 39 – 42 , 45 ].

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https://doi.org/10.1371/journal.pone.0307366.g001

3 Average BLER analysis

3.1 Average BLER analysis of Primary Receiver (PR)

3.1.1 statistical analysis of snr distribution..

3.1.2 Average BLER analysis.

Having obtained the CDF of γ PR in hand, we are next so excited to derive the average BLER of PR by making use of the relation in (11) .

Having achieved Theorem 1, we are interested in concluding that the average BLER of PR can be characterized by a unique function involving all elementary functions. Thus, it is feasible to use common integrated software packages (i.e., Matlab, Maple, or Mathematica) to dissect the average BLER performance by this function without any simulation or actual testing. Yet, it would be extremely meaningful to explore or answer the questions of whether is there any simpler way to characterize the average BLER performance limits at a high SNR regime and how much performance gain the system can be achieved compared to an uncoded transmission.

3.2 Average BLER analysis of IoT sensor receiver

3.2.1 statistical analysis of snr distribution..

3.2.2 Average BLER analysis.

Having obtained the CDF of γ IR in hand, we are next so excited to derive the average BLER of IR by making use of the relation in (11) .

4 Numerical results and discussions

This section provides some illustrative numerical results using Monte-Carlo simulations to validate our developed mathematical framework, where the number of used channel realization samples is 10 5 . Without loss of generality, we consider the specific parameters for Rayleigh channels as follows: λ PT,PR = 2, λ PT,BD = 4, λ PT,IR = 3, and λ BD,IR = 5 (channel modeling has been early described at Eq 1 ). Unless otherwise specified, the key simulation parameters related to packet designs and transmit SNR are set as follows: α = 0.5, L RX = 256 c.u, N PR = 300 bits, N IR = 80 bits, and Ψ = 25 dB.

Fig 2 shows the average BLER versus α . We look at the case where PT sends 300 bits of data to PR, while BD produces 80 bits of data for command control sync. It is observed that the error performance of PR remains constant with respect to α since its receiving signal does not gain any backscattering signal from BD. Meanwhile, IR’s error performance tends to reduce with a small value of α and then become saturated. This is because on the one hand, scaling up α improves the received SNR signal to decode c ( t ) in (6) but decreases the received SINR signal to decode x ( t ) in (5) on the other hand. Recall that the SIC procedure dominates the decoding process at IR. Taking these together therefore explains why increasing α does not yield any error performance improvement.

https://doi.org/10.1371/journal.pone.0307366.g002

Fig 3 explores the impact of block-length L RX on the average BLER. From the figure, we can see that while the error performance of PR reduces considerably with an increase in L RX , that of IR decreases relatively low. The reasons are interpreted as follows: 1) for PR, the received SNR given in (3) does not suffer from any SIC, which gives PR a chance to decode x ( t ) without interference. Thus, it is safe to conclude that the more block-length (channel use) of the information transmission, the higher the reliability of the communication channel. Recall that, such phenomenon completely accords the analysis for the developed expression in (16) . 2) for IR, its decoding process takes place in two phases of decoding x ( t ) and c ( t ), respectively. Thus, this process will increase an expected error during decoding c ( t ), making the error performance of IR to be reduced slowly. Recall that such a phenomenon can be directly explained from (29) , where increasing L PR reduces the exponential component but scales up the Meijer-G component accordingly.

https://doi.org/10.1371/journal.pone.0307366.g003

Fig 4 showcases the average BLER against data amount sent by PT and BD. As observed, conveying more data over a fixed channel use to the receiving node causes more errors during communication, thereby leading to an increase in the BLER trend. These trends are perfectly matched with our analyses for the expression in (16) and (29) . In this case, a more channel should be allocated to boost reliable communication. Yet, this assignment might not be beneficial to the systems as it is equivalent to an increase in the transmission latency. Therefore, it is necessary to consider balancing such configuration for each application critically.

https://doi.org/10.1371/journal.pone.0307366.g004

Fig 5 depicts the impact of Ψ on the average BLER. Overall, we can find that increasing Ψ significantly improves the average BLER of PR and the trend is linearly decreasing. Clearly, this observation perfectly matches up with the developed formula in (16) , where the error performance also becomes zero when we take into consideration 1/Ψ = 0. Meanwhile, varying PR only improves the BLER of IR in moderate SNR but saturates at high SNR, which perfectly agrees with the conclusion drawn on (30) . On this basis, in order to improve the BLER of IR, we should take care of both increasing Ψ in conjunction with an increasing number of block-lengths.

https://doi.org/10.1371/journal.pone.0307366.g005

5 Conclusion

In this work, we have studied the performance of symbiotic backscatter communication systems with short-packet transmissions. Particularly, aiming to characterize the system performance without performing any simulation, we derived closed-form approximate and asymptotic expressions of the average BLER for both the primary receiver and IoT sensor. These mathematical frameworks enable us to directly assess the system performance by the key parameters of the transmit power, fading parameters, data amount, and packet length. To ensure the correctness of the developed mathematical framework, we produced some illustrative simulation results based on the Monte-Carlo simulation while comparing the actual impact of system parameters on the BLER behaviour over the analysis outcome.

Supporting information

https://doi.org/10.1371/journal.pone.0307366.s001

• View Article
• Google Scholar
• 3. Le M, Huynh-The T, Do-Duy T, Vu TH, Hwang WJ, Pham QV. Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey. arXiv preprint arXiv:231010549. 2023;.
• PubMed/NCBI
• 26. Janjua MB, Arslan H. Survey on symbiotic radio: A paradigm shift in spectrum sharing and coexistence. arXiv preprint arXiv:211108948. 2021;.
• 47. Tse D, Viswanath P. Fundamentals of wireless communication. Cambridge university press; 2005.
• 48. Jeffrey A, Zwillinger D. Table of integrals, series, and products. Elsevier; 2007.
• 49. Prudnikov AP, Brychkov IA, Marichev OI. Integrals and series: special functions. vol. 2. CRC press; 1986.

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Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

• Open access
• Published: 30 August 2024
• Volume 6 , article number  470 , ( 2024 )

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• Bo Chang 1 ,
• Xinrong Zhang 2 ,
• Haiyi Bian 1 &
• Huaqiang Huang 1  

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Article Highlights

In the application of the irrigation monitoring system in the agricultural environment, an RSSI-based ranging method is proposed for node localization.

In the RSSI distance weighting correction stage, the difference between the measured distance of the beacon node and the actual distance is fully considered, and the relative error coefficient of the beacon node is analyzed and selected to obtain the accurate measurement distance.

Under the case of harsh network environment and limited positioning cost, the system achieves better positioning effect. Compared with the least squares method, the method has high positioning accuracy and small computational amount.

Avoid common mistakes on your manuscript.

1 Introduction

Agriculture is the basic industry in China. Our country attaches increasing importance to agricultural output value, and it is very important to continuously implement new technologies in agriculture, especially to develop water-saving irrigation under the condition of water resources shortage. The soil moisture monitoring system is one of the agricultural environment monitoring. Water-saving irrigation means precise irrigation, and the basis of precise irrigation is to monitor and locate soil moisture in irrigated areas. At present, the technologies used in agricultural environment include GPS (Global positioning system), WSN (Wireless sensor networks) and UAV (Unmanned aerial vehicle), et al. Restricted by the cost and environmental conditions, GPS is not easy to realize alone in the wireless sensor networks for soil moisture monitoring [ 1 ]. In recent years, some scholars have proposed many positioning algorithms, but the application conditions of these algorithms are harsh, that is, each positioning algorithm is aimed at positioning problems in different applications.

WSN can perceive, collect and wirelessly transmit the environmental information in the monitoring area in a self-organized and multi-hop manner [ 2 ]. It has become one of the data processing platforms with significant development and application potential, which is applied in many fields such as agricultural environment monitoring and soil moisture content [ 3 ]. The hardware composition of a sensor network system usually includes sensor nodes (End nodes), aggregation nodes (Router nodes), and management nodes (Coordinator). Positioning is an important supporting technology of WSN, and it is widely used in industry, agriculture, medical treatment and other fields. The most commonly used wireless communication protocol for building a WSN network is ZigBee, which is a close-range, low-power consumption, low-data transmission rate, low-cost bi-directional wireless communication technology, and can be embedded in various devices, while supporting the geolocation function. The application of ZigBee technology to WSN is a focus of current research, and the research and application of related positioning technology has also attracted wide attention. In practical application, we can also use the cruise UAV technology based on ground-air collaboration, which takes the UAV loading ZigBee coordinator as the gathering node, collect environmental information from the WSN nodes on the ground, and transmit the data back to the remote server in time [ 4 ]. The advantages of UAV are high efficiency, high coverage rate and intelligence, which are conducive to the realization of agricultural modernization. In recent years, many scholars have carried out relevant scientific research [ 5 ].

Whether using the WSN of the surface network, or the ground-air collaborative cruise UAV technology, the position coordinates of unknown nodes are an important feature of sensor acquisition information, so it is necessary to monitor node positioning. The features, such as rapid unfolding, robust fault tolerance, and long working life, make the application of WSN in the field of soil moisture monitoring develop very quickly, and many scholars have carried out relevant scientific research [ 6 ].

Range-based and distance-free positioning are the two widely used types of positioning methods [ 7 ]. The Range-Free way does not need to measure the distance or the angle, but it cannot meet the application requirements of high accuracy due to the more significant error. The Range-Based method requires information such as distance or angular orientation. Commonly used ranging algorithms include receiving signal strength indication (RSSI) [ 8 , 9 , 10 ], time of arrival (TOA) [ 11 , 12 ], time difference of arrival (TDOA) [ 13 , 14 ], angle of arrival (AOA) [ 15 , 16 ], etc. The latter three require additional equipment and high cost.

In [ 17 ], the author first explained the principle of the received signal strength indicator (RSSI) and the factors affecting the positioning accuracy, including the number and location of sensors of the factors, the quality of the received signal, the arrival Angle (AOA), and the arrival time difference (TDOA). Then the authors used the beacon nodes and the arrival distance for the sensor positioning. The simulation results showed that the system obtains lower mean estimation error when using RSSI because of temporal synchronization, which indicated that RSSI-based measurements achieve higher accuracy and accuracy during localization. However, because the RSSI values are different in different regions or directions in the environment, the adverse effect is mainly reflected in the path loss factor. The fixed empirical value of path loss will reduce the range accuracy due to the change in the signal propagation area; and the solution is to use multiple measurements and cycle refinement to increase the range precision [ 18 ].

At present, some scholars have carried out research from different perspectives or applications, and have proposed many positioning algorithms based on RSSI ranging. Before positioning, the RSS distance measurement needs to be conducted first, and then the distance model is established. In order to improve the ranging accuracy of RSS distance, in [ 19 ], the authors proposed a distribution positioning method based on RSSI, which can achieve high positioning accuracy without dense deployment of nodes, and also analyzed the distribution attributes of different nodes under fine-grained distance and constructed a unit positioning model. The results showed that the localization accuracy is improved by 50% compared with the existing methods, and the error is less than 1.5 m. RSSI-based ranging accuracy depends on environmental and weather conditions, so the authors in [ 3 ] analyzed and evaluated RSSI-based ranging and adaptive techniques in outdoor wireless sensor networks to improve ranging quality. In this article the authors highlighted the impact of path loss index estimation error and temperature variation on RSSI ranging and proposed an RSSI-based adaptive ranging algorithm to improve ranging quality under changing outdoor conditions. The algorithm includes link RSSI estimation, temperature compensation, PLE estimation, and inter-node distance estimation, better localization effects were obtained by evaluating the performance of the proposed algorithm. In [ 20 ], aiming at the common non-line-of-sight (NLOS) problem, considering the actual dynamics of ultra-low power indoor wireless channel environment, the authors proposed an indoor positioning algorithm that can automatically adapt to the environmental dynamics in real time. In [ 21 ], the authors proposed a fingerprint technology based RSS to achieve indoor wireless positioning, which uses a machine learning algorithm to take the RSS measurement as a location-related signal parameter to estimate the location of the target. In this method, the estimation is divided into two phases, the offline phase and the online phase. In the offline phase, RSS measurement vectors are first collected and generated from the radio frequency (RF) signal received at the fixed reference position, and then RSS radio maps are constructed. In the online stage, the reference location is searched by the radio map to find the instantaneous location of the indoor target. However, the factors such as multipath, diffraction, and obstacle occlusion in the practical application environment often bring errors to the RSSI-based range.

By establishing the RSS filtering model, Ranjan et al. [ 22 ] applied the Gaussian average filtering technology to locate the estimation, which can better suppress the measurement noise, reduce the estimation error, and obtain a satisfactory positioning effect. By installing the measuring equipment, Katwe et al. [ 23 ] and Tomic et al. [ 24 ] adopted a hybrid ranging positioning scheme, and also obtained good positioning results based on the effective estimation algorithm. Under the RSS measurement condition, Ahmad et al. [ 25 ] achieved a good balance of positioning speed and algorithm quality by selecting the number of sensors involved in positioning calculation, but this algorithm comes at the cost of economy. Moreover, they made practical tests of the proposed positioning algorithms from the perspectives of calculation complexity, ranging, and positioning error, conducted a detailed analysis, and made the positioning performance evaluation. The re-search on the above positioning algorithms has a positive significance. Although these algorithms have a good positioning effect to a certain extent, the common problems are that they have high computational costs and high communication requirements. Therefore, these algorithms are difficult to popularize in soil moisture monitoring.

To overcome the shortcomings encountered by the above positioning algorithm, on the premise of not increasing the hardware facility and meeting the positioning accuracy of the monitoring system, we should deeply explore the application requirements of the RSSI positioning methods and monitoring systems. Under the needs of the positioning system, we should adopt a feasible scheme to reduce the positioning cost on the premise of preserving the positioning function. Our proposed algorithm assumes distance weighting and coordinate correction, reducing the system power consumption and prolonging network life. Moreover, the software cost is relatively small, and its positioning function is suitable for the application requirements of the soil moisture monitoring system.

2 RSSI ranging modeling

2.1 wireless channel modeling.

In the soil environment, there are different soil textures and other organisms with dense and uneven distribution, which will lead to multiple paths, diffraction, and obstacle shielding, complicating the RF signal transmission model. The monitoring node itself can provide RSSI measurements without adding additional hardware devices. Since the path-loss of radio wave transmission has a significant impact on the RSSI measuring precision, a log-normal topology model is used in this system. RSSI values are expressed as follows:

where \(P_{t}\) and \(K_{t}\) are the transmit power and the antenna gain, respectively. \(P_{{{\text{loss}}}} (l)\) is the path loss after the distance ( l ).

where \(P_{loss} (l_{0} )\) is the consumed power after the ranges \(l_{0}\) . \(\rho\) is the path loss index. \(l_{0}\) is the referred range, and its value usually is 1 m. Substituting formula ( 2 ) into formula ( 1 ), there is

thus, \(P_{r} (l)\) is given by

where \(P_{r} (l_{0} )\) is the RSS (Received Signal Strength) value at l 0 . The larger the \(P_{r} (l)\) value measured by the monitoring node is, and the closer the distance is, the smaller the error caused by the \(P_{r} (l)\) deviation is.

After the referred ranges l 0 , \(P_{loss} (l_{0} )\) is given by

The meaning of K t is given as described in formula ( 1 ), K r is the aerial receipt enhancement. L is the disadvantage factor, and λ is the wireless signal wavelength.

2.2 Internode distance estimation

It is assumed that the positioned nodes are deployed uniformly and randomly in the wireless monitoring region. Each node has the same transmission range, and the transmission range can be represented as a regularly rounded region. If we take \(l_{0} = 1\) m, by formula ( 3 ), then \(P_{r} (l)\) is given by

If enough nodes are located within the transmission range of the monitoring node, according to the interrelation between \(P_{r} (l)\) and the distance l , we assume that the minimum received signal intensity is \(P_{rmin}\) corresponds to the maximum distance \(l_{max}\) , then

The symbol l max is considered to be the transmission radius. Among the multiple RSSI values obtained in monitoring the unknown nodes, the minimum RSSI value is \(P_{rmin}\) , corresponding to l max  = R; thus, we can get the l value from the unknown node to the beacon point. The search method of \(P_{rmin}\) is to put all the RSSI values received by the monitoring node to be positioned together with all the RSSI values obtained by the neighbor node, ranking from large to small and taking the smallest RSSI value as \(P_{rmin}\) .

2.3 Distance ranging correction between nodes

To obtain the measurement error of RSSI values, the case of a beacon node with the known location is first considered. By measuring the \(P_{r} (l)\) value of the beacon node with the known sites in the network, we use the RF signal attenuation modeling to calculate the measured distance value and then calculate the actual distance between the beacon nodes according to the exact coordinates of the beacon node, and compare the measured length with the exact length, thus obtain the measurement error of the beacon node \(P_{r} (l)\) measurement value. When ranging the unknown monitoring node, considering the measurement error of the \(P_{r} (l)\) value, the adverse effects of various random factors in the monitoring network on the RSSI ranging results can be decreased.

For the 2-D situation, we assume that the beacon node is marked as \(B_{j} \left( {x_{j} ,{ }y_{j} } \right)\) , \(j = 1,{ }2, \cdots ,M\) , M is the total number of beacon nodes participating in the correction process. \(B_{0} (x_{0} ,{ }y_{0} )\) represents the beacon node to be revised. The physical distance between \(B_{0} (x_{0} ,y_{0} )\) and \(B_{j} \left( {x_{j} ,{ }y_{j} } \right)\) is recorded as \(d_{j}\) . The lengths obtained by \(P_{r} (l)\) are recorded as \(l_{j}\) . The RSSI distance-measured relative error is recorded as:

Then the correction coefficient for the relative error of weighted distance at the beacon node \(B_{j} \left( {x_{j} ,{ }y_{j} } \right)\) is recorded as:

The coefficient \(\eta_{w}\) indicates the RSSI measuring deviation of the beacon node. Considering the weighting occupied by the various \(P_{r} (l)\) , the smaller the range among the monitoring nodes, the smaller the range inaccuracy induced by the deviation of \(P_{r} (l)\) , and the larger the weighting to the compensation quotient. Then the compensation range of the beacon node is given by

where \(l_{uj}\) is the measured range between node \(B_{0}\) and the beacon node \(B_{j}\) , \(l_{uj}^{c}\) is the corresponding compensation range between two nodes.

3 Unknown node coordinate estimation

3.1 weighted centroid localization.

According to the RF signal transmission modeling discussed above, the larger the RSSI value is, the closer the distance between the monitoring nodes is, the farther the space is, and vice versa. As the node to be measured is closer to the beacon node, the measured RSSI value leads to a higher ranging accuracy, that is, the higher the confidence. When the distance is more significant than a particular threshold value, the ranging error caused by the RSSI value will increase, and the credibility of the RSSI value will decrease. Therefore, it is pretty reasonable to propose a centroid positioning method on the basis of RSSI distance weighting.

The algorithm achieves the weight of each beacon node to the centroid coordinates by the size of the weighted coefficients. The larger the RSSI value is, the smaller the distance between nodes is, the higher the confidence of the RSSI value is at this time, and the greater the weight of the centroid coordinates is. Therefore, we can choose the appropriate weighting coefficient to perform the RSSI weighting calculation to improve the positioning accuracy.

Assuming that the unknown node U 1 receives the RSSI values of the three beacon nodes B 1 , B 2 , and B 3 within the communication range of the node U 1 , these values are marked as R 1 , R 2 , and R 3 , and we have obtained the path loss index \(\rho\) , which can be calculated from formula ( 8 ), in the monitoring range of the node U 1 . After considering the RSSI value and the distance weighting factor, the coordinate ( x , y ) calculation formula of the node U 1 can be given by

l 1 , l 2 , and l 3 are the measured distances from U 1 to B 1 , B 2 , and B 3 , respectively, and k is the weighted adjusting coefficient. In practical application, we can adjust the level of the weighting calibration by regulating the value of k , so that the localization system can achieve optimal effectiveness. The algorithm is characterized by a small computational amount and no additional communication overhead, which reduces the adverse effects of random noise in RF signal modeling and thus improves the positioning accuracy of the monitoring nodes.

We then consider the angle information when selecting beacon nodes to form a weighting, which can further save computational resources and reduce positioning error. When using the three-sided measurement method to calculate the node position, it is required to keep the three points not in a straight line as far as possible; that is, to achieve a good positioning effect, the three angles are controlled as sharp as far as possible. Therefore, we consider setting the confidence degree; the greater the confidence degree, the better the positioning effect. For each of the \(C_{M}^{3}\) combinations of beacon nodes, let its confidence be \(CL_{ABC} (i)\) . We have

where \(\theta_{{A_{i} }}\) , \(\theta_{{B_{i} }}\) , and \(\theta_{{C_{i} }}\) are the three inner angles of the triangle formed by the three beacon nodes, then

The coordinates ( \(\hat{x},{ }\hat{y}\) ) of the node U 1 after weighting are calculated as

In the above three formulas, \(i = 1, 2,{ } \cdots ,C_{M}^{3}\) , M represents the total number of beacon nodes.

3.2 Node location correction

The ranging correction coefficient \(\eta_{w}\) can improve the accuracy of RSSI ranging, but it is unable to monitor the coordinate errors caused by various random factors, such as measurement equipment and emergencies. Therefore, we should also make full use of the known location information of the beacon nodes to correct the node positioning coordinates to improve the positioning accuracy further. Assuming that the beacon node location is unknown, the integrated weighted centroid localization algorithm proposed in this paper is used to calculate the beacon node coordinates and then to find the difference with the actual coordinates of the beacon nodes; thus, the error information of the beacon node coordinate is obtained. We must consider such coordinate error information to reduce further the influence of various random factors in the monitoring network on localization accuracy.

The assumptions made here are the same as those made in Sect.  2.3 . Assuming a beacon node \(B_{0} (x_{0} ,y_{0} )\) , whose location is unknown, and the other beacon nodes be recorded as \(B_{j} \left( {x_{j} ,{ }y_{j} } \right)\) , where \(j = 1,{ }2, \cdots N\) , N is the number of beacon nodes participating in the calculation of positioning error. The position of \(B_{0} \left( {x_{0} ,{ }y_{0} } \right)\) is calculated as \(B_{r0} \left( {x_{r0} ,{ }y_{r0} } \right)\) based on the distance between \(B_{j} \left( {x_{j} ,{ }y_{j} } \right)\) to \(B_{0} \left( {x_{0} ,{ }y_{0} } \right)\) , and the coordinate errors are obtained when compared with their actual coordinates. Then the coordinate error of the beacon node \(B_{0} \left( {x_{0} ,{ }y_{0} } \right)\) is expressed as

The position error of the j th beacon node is expressed in the following standard form

Weighted position errors within the monitoring region are the statistical mean of the position errors of the N beacon nodes and can be expressed as

where \(l_{jc}\) is the correction distance of the j th beacon node. The weighted coordinate errors \(e_{{w_{x} }}\) and \(e_{{w_{y} }}\) within the monitored region are the weighted average of the beacon node coordinate errors, reflecting the regional localization capability of the system. Let \(\left( {x_{r} ,{ }y_{r} } \right)\) be the coordinate values calculated by the weighted centroid localization algorithm for the unknown nodes; therefore, the coordinates \(\left( {\hat{x}_{r} ,{ }\hat{y}_{r} } \right)\) of the unknown nodes in the positioning system after being corrected by the regional positioning error coefficient are

4 Experimental verification

4.1 simulation and the analysis.

In simulation research and analysis, to reflect the influence of the quantity, density, and communication radius of beacon nodes on the positioning error of unknown nodes, we take the average positioning error of unknown nodes as the primary evaluation standard in the execution of the positioning algorithm. In the monitoring region, the positioning error of the j th node is expressed as \(E_{j}\) .

where, j  = 1, 2, …, \(M_{U}\) , \(M_{U}\) is the quantity of unknown locations nodes in the monitoring region, and the communication radius of the nodes is \(R_{j}\) , \(p_{j} = \left[ {\begin{array}{*{20}c} {x_{rj} } & {y_{rj} } \\ \end{array} } \right]^{T}\) is the final estimated position of node j , and \(z_{j} = \left[ {\begin{array}{*{20}c} {x_{j} } & {y_{j} } \\ \end{array} } \right]^{T}\) is the actual position of node j . The mean of the positioning error for all nodes of unknown locations is marked as E a

Obviously, the smaller the value of \(E_{a}\) , the higher the localization accuracy.

4.1.1 Network environment

MATLAB 2020a is selected as the simulation test platform, and the simulation environment is set to a rectangular area of 80 m \(\times\) 80 m. In this paper, the log-normal model is used as the RF communication ranging model between nodes, shown in formula ( 2 ). In the ranging model, the RSSI values and distance l are the inputs and outputs of the model, respectively. Various random disturbances in the actual monitoring environment lead to some ranging errors. For the simul a tion, the inter-node spacing superimposed on the Gaussian noise was used as the RSSI input in the wireless communication model. Inter-node spacing is calculated from the actual coordinates of the monitoring nodes. The standard deviation of the Gaussian noise is \(\sigma_{i}\) , and its expression is shown in formula ( 22 ),

where, j  = 1, 2, …, \(M_{U}\) , \(R_{max}\) indicates the furthest propagation range of the monitored node, \(R_{j}\) indicates the propagation range of the j th monitored node, \(\eta_{j}\) indicates the distance-measured relative error of the j th monitored node, which is defined as in formula ( 9 ). We can imitate different ranging errors by regulating the value of \(\eta_{j}\) .

In the following simulation experiments, the localization effects of three algorithms are listed. Where algorithm A indicates the presented algorithm, algorithm B indicates the LS (Least Square) method and algorithm C indicates the standard centroid positioning (SCP) method. The positioning accuracy of the three algorithms in different ranging errors, the number of varying beacon nodes, and the communication radii of other nodes are compared and analyzed by simulation.

4.1.2 Relationship between position accuracy and ranging error

We set the number of beacon nodes to \(M\) =15 and the total number of nodes to 300. The obtained simulation results are shown in Fig.  1 .

Effects of distance measurement error on localization accuracy

According to Fig.  1 , the localization precision of three algorithms decreased with the increasing variance of ranging error. The ranging error has the most significant influence on algorithm C, and when the variance of range error is significant, the localization precision decreases more. Algorithm B is less affected by range error, but in contrast, the proposed algorithm A suppresses the range error very well, thus achieving a higher localization precision.

When the variance of ranging error is \(\sigma_{j}^{2} = 0.1\) , the positioning accuracy of algorithm B is about 0.21, and that of algorithm C is about 0.26; when the variance \(\sigma_{j}^{2}\) increases, the localization precision of the three algorithms begins to decrease, while the localization precision of algorithm A is invariably superior to that of the other algorithms. The reason is that when the variance of ranging error \(\sigma_{j}^{2} = 0.1\) , the localization precision is mainly caused by the ranging error; after the ranging correction, the node localization precision has been dramatically improved. When the variance of the ranging error \(\sigma_{j}^{2}\) increases, \(\sigma_{j}^{2}\) significantly weakens the localization accuracy. The ranging correction of algorithm A acts as an inhibitory error and a significant improvement in localization accuracy.

4.1.3 Relationship between the localization accuracy and the number of beacon nodes

We set up the emulation environment of 80 m × 80 m, and eighty nodes presented with a random distribution. The communication distance of the node is 40 m. The obtained simulation results are shown in Fig.  2 .

Effect of the number of beacon nodes on the positioning precision

It can be seen from the curve trend shown in Fig.  2 that the positioning accuracy of the three algorithms increases with the number of beacon nodes, which is because the coordinate information provided by multiple beacon nodes can be verified and supplemented by each other, reducing the inaccurate positioning caused by errors in a single anchor node. And under the same conditions, the proposed algorithm (Algorithm A) outperforms the other two algorithms.

4.1.4 Relationship between the positioning precision and the communication radius of the nodes

We set the simulation environment as in the previous case, where 80 nodes are stochastically distributed in the 80 m × 80 m region and set that the quantity of beacon nodes is 10. The simulations yielded the results shown in Fig.  3 .

Relationship between node communication radius and positioning precision

Figure  3 illustrates that the positioning precision also gradually increases as the node communication radius increases. Because the more prominent the distance between the nodes communicates, the amount of information between the unknown node and the beacon node increases, and the distance error between the unknown node and the beacon node decreases. At the same time, due to the increased communication distance of the nodes, the beacon nodes around the unknown nodes also begin to grow, so the unknown nodes can use more beacon node distance to correct their space to the beacon node. Therefore, as the node communication distance increases, the positioning accuracy is also gradually increasing. Figure  3 shows that the positioning accuracy of algorithm A is higher than the other two algorithms under the same network conditions.

4.2 Experiment verification

To verify the localization effect of the proposed algorithm, in the about 15 m × 15 m area of the laboratory building, we built a small WSN experimental system using CC2530 nodes. Although the experimental area is limited compared with the actual scale of soil moisture monitoring applications, the experimental results can still be of great significance, and high-quality data can be obtained in a small range, which can be statistically extended to a larger range, provided that the corresponding assumptions are met. Here we assume that the node density per unit area is the same, so the node positioning performance is basically independent of the area size, while considering the uniform ductility of the region selected by the validation experiment, its size basically does not affect the validity of the results. There are five beacon nodes in the design, which are evenly arranged within the observed region. In addition, nine unknown nodes and one convergence node are manually set. The minimum communication distance of these nodes is about 10 m, and the nodes are placed directly on the ground and are about 0.1 m from the ground height. The data is transmitted every 10 s, and the mean of 20 tests is taken as the experimental results. The node to be positioned is considered an unknown node and is measured and placed after the specific location of the deployment setting. The resulting data are shown in Table  1 .

The E j in the table can be calculated from formula ( 20 ). We know from Table  1 that the minimum localization deviation of the algorithm in this paper (algorithm A) is 0.09, the maximum localization deviation is 0.21, and the average value is 0.16 in the actual monitoring environment. If the conditions are the same, the average localization deviation obtained by the simulation was 0.12. The reason why the actual positioning effect is inferior to the simulation effect is that the RF signal in the actual environment is blocked by indoor walls and electrical equipment, and the signal transmission loss and multipath reduce the measurement accuracy of RSSI, thus increasing the positioning error. In comparison, these interference factors are not considered in the simulation. In conclusion, we experimentally verified the feasibility of algorithm A for localization accuracy requirements.

We use the localization algorithm proposed in this paper to analyze 15 series of test data, where the Gaussian random variable is set as \(\xi_{\sigma } \left( {0,10} \right)\) , eighty nodes are randomly placed within the 80 m × 80 m region, and the communication radius of the nodes is 40 m, and the number of beacon nodes n = 20. To minimize the random deviation, 100 simulation experiments were done under the same conditions, averaging the results of 100 times, and obtaining 15 localization errors for 15 sets of corresponding unknown nodes, as shown in Fig.  4 .

Positioning error of the test data

In Fig.  4 , the maximum value of the positioning error is 0.35, the minimum value is 0.19, and the overall positioning effect is good. Because the 20 beacon nodes are evenly deployed in the laboratory test area, the positioning error of the area edge is slightly larger. If we deploy more beacon nodes at the edge of the test area, the localization effect will improve.

5 Conclusions

In this paper, we propose a weighted centroid localization algorithm applied to wireless monitoring systems of soil moisture. When designing the algorithm, we firstly used the signal propagation attenuation to build the model, obtained the range information by measuring the RSSI value, then selected the relative error correction coefficient of weighted distance to correct the measured range, and finally designed an integrated weighted centroid localization algorithm with less computation to reckon the location coordinates of the nodes to be tested. The advantage of this algorithm is that it can meet the accuracy of soil moisture wireless monitoring without adding hardware equipment. Simulation experiments indicate that the algorithm can efficiently suppress Gaussian noise with small transmission overhead. Simulation shows that the proposed algorithm is better than LS method and SCP method in positioning accuracy because of the distance error correction measure of RSSI level in the ranging stage, which reduces the measurement error and improves the positioning accuracy. Therefore, the proposed algorithm in this paper has a certain reference function for the practical application of soil moisture monitoring.

The research direction of positioning algorithm in agricultural environment monitoring is diversified, aiming at improving monitoring accuracy, reducing cost, enhancing system adaptability and intelligence level. Our future research direction is still the positioning algorithm with high precision and cost-reduction.

Data Availability

The datasets used during the current study are available from the corresponding author upon reasonable request.

Ndjuluwa LNP, Adebisi JA, Dayoub M. Internet of Things for crop farming: a review of technologies and applications. Commodities. 2023;2(21):367–81.

Article   Google Scholar  

Isaiaand C, Michaelides MP. A review of wireless positioning techniques and technologies: from smart sensors to 6G. Signals. 2023;4:90–136.

Luomala J, Hakala I. Analysis and evaluation of adaptive RSSI-based ranging in outdoor wireless sensor networks. Ad Hoc Netw. 2019;87:100–12.

Sakthivel S, Vivekanandhan V, Manikandan M. Automated irrigation system using improved fuzzy neural network in wireless sensor networks. Intell Autom Soft Comput. 2023;35(1):853–66.

Wenting H, Liyuan Z, et al. Research progress of the application of UAV remote sensing technology in Precision Irrigation. Journal of Agricultural Machinery. 2020;51(2):1–14.

Google Scholar  

Korošak Ž, Suhadolnik N, Pleteršek A. The implementation of a low power environmental monitoring and soil moisture measurement system based on UHF RFID. Sensors. 2019;19(24):5527. https://doi.org/10.3390/s19245527 .

Aziz MAE. Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm. Wireless Network. 2017;23(2):1–9.

Kwon Y, Kwon K. RSS ranging based indoor localization in ultra-low power wireless network. Electron Commun. 2019;104:108–18.

Wang W, Liu X, Li M, et al. Optimizing node localization in wireless sensor networks based on received signal strength indicator. IEEE Access. 2019;7:73880–9.

Loganathan A, Ahmad NS, Goh P. Self-adaptive filtering approach for improved indoor localization of a mobile node with ZigBee-based RSSI and odometry. Sensors. 2019;19(21):4748. https://doi.org/10.3390/s19214748 .

Zhang H, Qi X, Wei Q, et al. TOA NLOS mitigation cooperative localisation algorithm based on topological unit. IET Signal Proc. 2021;14(10):765–73.

Wu S, Zhang S, Huang D. A TOA­based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination. IEEE Sensors Lett. 2019;3(3):1–4.

Bottigliero S, Milanesio D, Saccani M, et al. A low-cost indoor real-time locating system based on TDOA estimation of UWB pulse sequences. IEEE Trans Instrum Meas. 2021;70:1–11.

Dai Z, Wang G, Jin X, et al. Nearly optimal sensor selection for TDOA-based source localization in wireless sensor networks. IEEE Trans Veh Technol. 2020;69(10):12031–42.

XUS. Optimal sensor placement for target localization using hybrid RSS, AOA and TOA measurements. IEEE Commun Lett. 2020;24(9):1966–70.

Chang SM, Li YM, Yang XJ, Wang H, Hu WF, Wu YQ. A novel localization method based on RSS-AOA combined measurements by using polarized identity. IEEE Sens Journ. 2019;16(4):1463–70.

Sathish K, Chinthaginjala R, Kim W, Rajesh A, Corchado JM, Abbas M. Underwater wireless sensor networks with RSSI-based advanced efficiency-driven localization and unprecedented accuracy. Sensors. 2023;23:6973–87.

Nguyen HD, Wood IA. A block successive lower-bound maximization algorithm for the maximum pseudo-likelihood estimation of fully visible Boltzmann machines. Neural Comput. 2016;28(3):485–92.

Article   MathSciNet   Google Scholar  

Xia W, Liu W. Distributed adaptive direct position determination of emitters in sensor networks. Signal Process. 2016;123:100–11.

Kwon Y, Kwon K. RSS ranging based indoor localization in ultralow power wireless network. AEU: Archiv fur Elektronik und Ubertragungstechnik: Electron Commun. 2019;104:108–18.

Yaro AS, Maly F, Prazak P. A survey of the performance-limiting factors of a 2-dimensional RSS fingerprinting-based indoor wireless localization system. Sensors. 2023;23:2545–70.

Ranjan KM, Shet NSV. Localization based on RSSI exploiting Gaussian and averaging filter in wireless sensor network. Arab J Sci Eng. 2018;43:4145–59.

Katwe M, Ghare P, Sharma PK, et al. NLOS error mitigation in hybrid RSS-TOA-based localization through semi-definite relax-atio. IEEE Commun Lett. 2020;24(12):2761–5.

Tomic S, Beko M, Tuba M. A linear estimator for network localization using integrated RSS and AOA measurements. IEEE Signal Process Lett. 2019;26(3):405–9.

Ababneh AA. Density-based sensor selection for RSS target localization. IEEE Sens J. 2018;18(20):8532–40.

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Acknowledgements

We would like to thank the faculty of electronic engineering of Huaiyin institute of technology for providing us with the experimental site. We are also grateful to thank the lab teachers from the internet of things laboratory of Huaiyin Institute of technology for their help.

This research was funded by the National Natural Science Foundation of China, grant number 62205120.

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Bo Chang, Haiyi Bian & Huaqiang Huang

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The authors confirm contribution to the paper as follows: study conception and design: BC, XZ; data collection: BC, XZ, HH; analysis and interpretation of results: BC, XZ; draft manuscript preparation: BC; review and editing, and funding acquisition, HB; review and editing, HH. All authors reviewed the results and approved the final version of the manuscript.

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Chang, B., Zhang, X., Bian, H. et al. Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement. Discov Appl Sci 6 , 470 (2024). https://doi.org/10.1007/s42452-024-06166-9

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Received : 30 April 2024

Accepted : 21 August 2024

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DOI : https://doi.org/10.1007/s42452-024-06166-9

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• Soil moisture monitoring ranging
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• Weighted centroid localization
• Localization precision

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