Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation

Ding, Weizhong; Zhong, Qiubo; Wang, Yan; Guan, Chun; Fang, Baofu

doi:10.3390/s22030733

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…The simulation result shows that the maximum operational distance for the proposed array antenna’s beam gain is more than 25 m (m), whereas the minimum distance is also higher than 22 m (m), illustrated in Figure 14 . For the single ESPAR antenna gain 8.17 dBi, the distance is also higher than 17 m. Compared to the previously published papers beam gains, such as 7.99 dBi [ 13 ] and 6 dBi [ 24 ], the RSS result is also depicted in Figure 14 . It is shown that the distance due to the lower beam gain is less than the distance obtained due to the higher gain proposed antennas.…”

Section: Sir Improvement and Range Extensionmentioning

confidence: 80%

“…Different techniques could be used to find out the direction of tags; among them, beam scanning is an efficient method [ 1 , 13 , 15 , 47 ]. The ESPAR antenna cannot calculate the AoA of the incident beam by itself which is why it uses the beam scanning method as illuminated in Figure 9 a.…”

Section: Beam Scanning Methods (Bsm)mentioning

confidence: 99%

“…Each sensor network with limited power consumption and higher efficiency becomes an important issue [ 5 , 6 , 7 ]. In addition, interference is a major obstacle to obtaining peak performance of the sensor networks [ 8 , 9 , 10 ] because the interference is exhibited within the network due to multipath fading or reflections which strongly affects the signal strength received by the radio frequency identification (RFID) reader antenna [ 11 , 12 , 13 ]. Likewise, the WSN system’s performance could degrade due to the minimum value of the signal-to-interference ratio (SIR) or the signal-to-interference and noise ratio (SINR) [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

Rahman

Ryu

2022

Sensors

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Wireless sensor networks (WSNs) systems based on Internet of Things (IoT) have developed rapidly in recent years. However, interference is a major obstacle to relatively long-distance communications in such networks. It is also very complicated and challenging to fix the exact location of tags in the IoT sensor networks. To overcome these problems, in this paper, an electronic steering parasitic array radiator (ESPAR) antenna used as a beamformer to handle the interference and extend the communication range from the sensors or tags is suggested. In addition, an efficient method, namely beam scanning (BS), is proposed to find the directions of tags. The beam scanning method (BSM) can be used for the selective beam switching (SBS) system by designing an ESPAR or array of ESPAR antennas with the help of CST studio. The antennas exhibit higher gain (8.17 dBi, 11.40 dBi) and proper radiation pattern at a particular direction. In addition, the MATLAB simulation findings indicate that the proposed BSM algorithm provides longer communication range, i.e., 25 m. In order to maximize range while avoiding interference, it is necessary to determine the direction and precise orientation of the tag in the WSN communication systems. Consequently, this work could be applied to an IoT sensor network such as an electrocardiogram system by providing better advantages such as higher localization accuracy and longer operating range.

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Section: Sir Improvement and Range Extensionmentioning

confidence: 80%

Section: Beam Scanning Methods (Bsm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

Rahman

Ryu

2022

Sensors

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“…By utilizing an arithmetic optimization algorithm, the model aims to optimize the deployment of sensor nodes, which can potentially lead to improved localization performance. [4] addresses the problem of target localization in wireless sensor networks (WSNs) using received signal strength (RSS) measurements and convex relaxation techniques. The authors propose a method that estimates the location of a target based on the RSS values collected from multiple sensor nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

Abed Salman,

A. Mahdi

2024

Iraqi Journal For Computer Science and Mathematics

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Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includesfour steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor startsbroadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possiblelocation where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing themeasuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchorsaccording to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts ininitialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it forfuture use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational modeattaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, withlower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar MeanAbsolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer amongthe neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared toMTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers aneffective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressingthe correlation between wireless device positions and signal intensities to improve the localization process. Theobtained results and provided justification emphasize the significance and value of the model in the field oflocalization in WSNs. The model represents a valuable contribution to the development of localization techniquesand improving their accuracy to meet the needs of various applications. The model opens up opportunities for itsutilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disastermanagement, enhancing its practical applications and practical significance.

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NLOS error mitigation in TOA systems

Chen,

Wang,

et al. 2024

Wireless Netw

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Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation

Cited by 5 publications

References 40 publications

IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

IoT Sensor Network Using ESPAR Antenna Based on Beam Scanning Method for Direction Finding

Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

NLOS error mitigation in TOA systems

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