Velocity adaptation based PSO for localization in wireless sensor networks

Nagireddy, Vyshnavi; Parwekar, Pritee; Mishra, Tusar Kanti

doi:10.1007/s12065-018-0170-4

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…The work in [121] focuses on a swarm intelligence based Particle swarm optimization algorithm using velocity adapta-tion to minimize the localization error in WSNs.…”

Section: Deployment and Localization Solutions Based Ai Techniques In...mentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

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“…The work in [121] focuses on a swarm intelligence based Particle swarm optimization algorithm using velocity adapta-tion to minimize the localization error in WSNs.…”

Section: Deployment and Localization Solutions Based Ai Techniques In...mentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

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“…In WSNs, PSO has been used to address several problems such as energy conservation [39], coverage maximization [40], optimal deployment of sensors [41], clustering, clustering head selection, data aggregation [42], and node localization [43].…”

Section: A Tdma Scheduling Optimizationmentioning

confidence: 99%

A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks

Elrefaei¹,

Yahya²,

Shaat³

et al. 2021

IJACSA

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In time-sensitive applications, such as detecting environmental and individual nuclear radiation exposure, wireless sensor networks are employed.. Such application requires timely detection of radiation levels so that appropriate emergency measures are applied to protect people and the environment from radiation hazards. In these networks, collision and interference in communication between sensor nodes cause more end-to-end delay and reduce the network's performance. A time-division multiple-access (TDMA) media access control protocol guarantees minimum latency and low power consumption. It also overcomes the problem of interference. TDMA scheduling problem determines the minimum length conflict-free assignment of slots in a TDMA frame where each node or link is activated at least once. This paper proposes a meta-heuristic centralized contention-free approach based on TDMA, a modified particle swarm optimization. This approach realizes the TDMA scheduling more efficiently compared with other existing algorithms. Extensive simulations were performed to evaluate the modified approach. The simulation results prove that the proposed scheduling algorithm has a better performance in wireless sensor networks than the interference degree leaves order algorithm and interference degree remaining leaves order algorithm. The results demonstrate also that integrating the proposed algorithm in TDMA protocols significantly optimizes the communication latency reduction and increases the network reliability.

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“…Besides those mentioned, there are also many other PSO implementations for node localization problems, for example, in Ref. [25] velocity adaptation based PSO for this problem was proposed, while in Ref. [26] localization accuracy was significantly improved by using a localization algorithm based on the PSO metaheuristics.…”

Section: Review Of Swarm Intelligence Metaheuristics For Wireless mentioning

confidence: 99%

Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

Strumberger

Minović

Tuba

et al. 2019

Sensors

105

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Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.

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Velocity adaptation based PSO for localization in wireless sensor networks

Cited by 21 publications

References 23 publications

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks

Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

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