PSO-Based Target Localization and Tracking in Wireless Sensor Networks

Lee, Shu-Hung; Cheng, Chia-Hsin; Lin, Chien-Chih; Huang, Yung-Fa

doi:10.3390/electronics12040905

Cited by 20 publications

(18 citation statements)

References 33 publications

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“…Figure 6 is the AFSA target tracking flowchart [30]. The mobile sensors are deployed in a 100 m × 100 m square wireless sensor network.…”

Section: Target Tracking Methodsmentioning

confidence: 99%

“…The DAFS method can reduce the search area and the number of AFs used in the algorithm to improve the efficiency of the target tracking system. Figure 9 shows the procedure of the DAFS method [30].…”

Section: Dynamic Af Selection Methodsmentioning

confidence: 99%

“…3.1.3. Region Segmentation Method [30] The region segmentation method (RSM) involves dividing a 100 m × 100 m square wireless sensor network into four equal zones and positioning an anchor node in the center of each zone. The mobile sensor is equally divided into four segments and placed in each region.…”

Section: Hybrid Adaptive Vision Of Preymentioning

confidence: 99%

“…In this study, the hardware and software used for simulations are listed in Table 1 [30]. The wireless sensor network is a two-dimensional area of 100 m × 100 m. The RSSI…”

Section: Simulation Environmentmentioning

confidence: 99%

“…In this study, the hardware and software used for simulations are listed in Table 1 [30]. The wireless sensor network is a two-dimensional area of 100 m × 100 m. The RSSI values will be calculated by substituting the position of each AF and the target point into the target function.…”

Section: Simulation Environmentmentioning

confidence: 99%

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Target Positioning and Tracking in WSNs Based on AFSA

Lee¹,

Cheng

Lin

et al. 2023

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In wireless sensor networks (WSNs), the target positioning and tracking are very important topics. There are many different methods used in target positioning and tracking, for example, angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). This paper uses an artificial fish swarm algorithm (AFSA) and the received signal strength indicator (RSSI) channel model for indoor target positioning and tracking. The performance of eight different method combinations of fixed or adaptive steps, the region segmentation method (RSM), Hybrid Adaptive Vision of Prey (HAVP) method, and a Dynamic AF Selection (DAFS) method proposed in this paper for target positioning and tracking is investigated when the number of artificial fish is 100, 72, 52, 24, and 12. The simulation results show that using the proposed HAVP total average positioning error is reduced by 96.1%, and the positioning time is shortened by 26.4% for the target position. Adopting HAVP, RSM, and DAFS in target tracking, the positioning time can be greatly shortened by 42.47% without degrading the tracking success rate.

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“…Figure 6 is the AFSA target tracking flowchart [30]. The mobile sensors are deployed in a 100 m × 100 m square wireless sensor network.…”

Section: Target Tracking Methodsmentioning

confidence: 99%

Section: Dynamic Af Selection Methodsmentioning

confidence: 99%

Section: Hybrid Adaptive Vision Of Preymentioning

confidence: 99%

“…In this study, the hardware and software used for simulations are listed in Table 1 [30]. The wireless sensor network is a two-dimensional area of 100 m × 100 m. The RSSI…”

Section: Simulation Environmentmentioning

confidence: 99%

Section: Simulation Environmentmentioning

confidence: 99%

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Target Positioning and Tracking in WSNs Based on AFSA

Lee¹,

Cheng

Lin

et al. 2023

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Path loss model based on exponential water cycle algorithm for wireless sensor network

Rayar,

Naik

2023

Int J Communication

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SummaryWireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient . The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.

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Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

Ramadevi,

Subramanyam,

Bindu

2024

Int J Communication

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SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

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PSO-Based Target Localization and Tracking in Wireless Sensor Networks

Cited by 20 publications

References 33 publications

Target Positioning and Tracking in WSNs Based on AFSA

Target Positioning and Tracking in WSNs Based on AFSA

Path loss model based on exponential water cycle algorithm for wireless sensor network

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

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