A new localization method based on improved particle swarm optimization for wireless sensor networks

Yang, Qiaohe

doi:10.1049/sfw2.12027

Cited by 25 publications

(9 citation statements)

References 18 publications

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“…DE algorithm, Particle Swarm Optimization (PSO) [57,58], Genetic Algorithm (GA) [59], and other bio-inspired evolutionary algorithms share the same challenge of easily getting trapped in local optima. The reason behind this lies in the fact that most bio-inspired evolutionary algorithms are similar as brute force search algorithms, conducting limited iterations within a population to find the optimal solution.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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“…In addition, [133] improved PSO algorithm called improved self-adaptive inertia weight particle swarm optimization (ISAPSO) to overcome the issue of losing diversity and getting trapped in local optima in standard PSO. The ISAPSO algorithm is based on the convergence conditions of PSO and retains the simplicity, ease of implementation, and low parameter adjustments of the original algorithm.…”

Section: Swarm Intelligence Is Another Category Of Optimization Algor...mentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

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“…The convergence rate and accuracy of suggested GA were more than the existing algorithms. Improved self-adaptive PSO algorithm was suggested in [ 72 ] to improve the accuracy and to reduce the power consumed by sensor nodes under various scenarios of error and sensor node values.…”

Section: Literature Reviewmentioning

confidence: 99%

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

2022

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Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

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A new localization method based on improved particle swarm optimization for wireless sensor networks

Cited by 25 publications

References 18 publications

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

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