Improved distance estimation with node selection localization and particle swarm optimization for obstacle-aware wireless sensor networks

Phoemphon, Songyut; So-In, Chakchai; Leelathakul, Nutthanon

doi:10.1016/j.eswa.2021.114773

Cited by 30 publications

(14 citation statements)

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“…In terms of specific algorithms, in the hybrid optimized localization algorithm (WOA-QT) [ 26 ], the co-evolutionary capability of the quasi-affine transform evolutionary algorithm (QUATRE) was fused with the whale optimization algorithm (WOA) to improve localization accuracy, but there was still the problem of falling into local optima. In the particle swarm optimization-based localization algorithm (IDE-NSL-AWSN) [ 23 ], ANs were grouped and cooperated with each other to increase the density of ANs, and then the particle swarm optimization algorithm (PSO) was used for localization to improve its localization accuracy and global search capability, but it still suffers from a lengthy structure and increased cost. While the Gaussian-modified RSSI algorithm and the improved whale optimization algorithm were used in the improved whale optimized localization algorithm (IWOA) [ 27 ] to reduce the ranging error and perform search localization, which improved the population diversity and node localization accuracy, there was still the problem of falling into local optimum.…”

Section: Related Workmentioning

confidence: 99%

“…Although RSSI-based positioning algorithms have structural and cost advantages under ideal conditions, their positioning accuracy are easily affected and significantly degraded by ranging errors in environments with high interference [ 23 ]. However, it is not only the anisotropic noise in the signal path that has an impact on the localization accuracy [ 24 ], but also the too small communication radius and sparse anchor nodes are the main causes.…”

Section: Introductionmentioning

confidence: 99%

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A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Zhu

2023

Sensors

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Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Zhu

2023

Sensors

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“…It applies differential evolution to optimize the localization result of the unknown node. Considering the impact of obstacles, the algorithm proposed in [15] divides the anchors into multiple groups. If the anchors of a group cannot accurately localize the unknown node, the other anchors in the nearby groups are used to assist localization.…”

Section: Related Workmentioning

confidence: 99%

Localization for Wireless Sensor Networks Assisted by Two Mobile Anchors with Improved Grey Wolf Optimizer

Cui

Zhao

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, the mobile anchor-assisted localization (MAL) algorithm seems promising. A MAL algorithm using a single mobile anchor has low energy consumption but a high localization error. Conversely, a MAL algorithm with three or more mobile anchors has minor localization errors but high energy consumption. By balancing energy consumption and localization accuracy, our study developed a localization algorithm assisted by two mobile anchors. A mobile anchor traverses the network along a double anchor SCAN (DASCAN) path, which divides the deployment region into grids and requires the two mobile anchors to traverse different horizontal lines in a zigzag pattern. Sensor nodes estimate their locations using a multiple-disturbance strategy grey wolf optimization (MDS-GWO) algorithm, which improves optimization by introducing a nonlinearly decreasing weight, a random perturbation of grey wolves and a mirror grey wolf. Using MATLAB, DASCAN was compared with GTURN, GSCAN, PP-MMAN, H-Curves, M-Curves, and SCAN paths by their energy consumption and localization rates. The localization error of MDS-GWO was compared with trilateration, PSO, WOA, and GWO. The impacts of radio irregularity, radio radius, and fading effect on MDS-GWO with different paths were also analyzed. The simulation results showed that the energy consumption of DASCAN was, on average, 30.1% less than GSCAN, GTURN, and PP-MMAN, but they had almost the same localization accuracy. The energy consumption of DASCAN was an average of 18.67% more than M-Curves, H-Curves, and SCAN, but the localization error of DASCAN was average of 32.3% less than SCAN, H-Curves, and M-Curves. The localization error of MDS-GWO was average of 25.5% less than trilateration, PSO, WOA, and GWO. Moreover, the performance of the proposed algorithm was less affected by different setups than the compared methods.

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“…The advantages of empirical path loss models are easier to implement than theoretical path loss models [6]. Moreover, stepwise regression, genetic algorithm, particle swarm optimization and machine learning are used to establish the empirical path loss model [6,[17][18]. However, due to the observations error of RSSI, the calculated path loss has the irregular fluctuations and discontinuity of adjacent areas.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model of regional path loss of wireless signals through the wall

2022

KSII TIIS

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Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

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Improved distance estimation with node selection localization and particle swarm optimization for obstacle-aware wireless sensor networks

Cited by 30 publications

References 50 publications

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks

Localization for Wireless Sensor Networks Assisted by Two Mobile Anchors with Improved Grey Wolf Optimizer

A hybrid model of regional path loss of wireless signals through the wall

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