Coverage Maximization in Wireless Sensor Networks Using Minimal Exposure Path and Particle Swarm Optimization

Bonnah, Ernest; Ju, Shiguang; Cai, Wenpeng

doi:10.1007/s11220-019-0266-7

Cited by 23 publications

(5 citation statements)

References 22 publications

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“…Considering that the environment of EMWSNs is usually more complicated and harsh, the simulation environment in this article is static deployment. In this case, sensor nodes can usually be deployed in the area to be monitored in a deterministic or random manner, and generally, no longer move after deployment [20].…”

Section: Emwsn Target Coverage Modelmentioning

confidence: 99%

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

Liu

Yang

et al. 2021

Journal of Sensors

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In recent years, more and more researchers have paid attention to the three-dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two-dimensional plane is full of loopholes when applied in the real three-dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three-dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9 % higher than that of GA and 5.4 % higher than that of SA. The network energy consumption of BACSFLA is 36.0 % lower than GA and 35.9 % lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.

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Section: Emwsn Target Coverage Modelmentioning

confidence: 99%

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

Liu

Yang

et al. 2021

Journal of Sensors

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“…They used virtual repulsive force and calculated the directional vector by virtual force. The authors of [11,39] applied Particle Swarm Optimization (PSO) to the coverage problem with this sensor model, and Reference [40] suggested Hybrid Genetic Particle Swarm Optimization (H-GPSO), which is a mixing model of the models in Reference [28] and [11]. The region considered in [9][10][11] does not include obstacles.…”

Section: Sensor Deploymentmentioning

confidence: 99%

Algorithms for Finding Vulnerabilities and Deploying Additional Sensors in a Region with Obstacles

Kim

Lee

2021

Electronics

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Consider a two-dimensional rectangular region guarded by a set of sensors, which may be smart networked surveillance cameras or simpler sensor devices. In order to evaluate the level of security provided by these sensors, it is useful to find and evaluate the path with the lowest level of exposure to the sensors. Then, if desired, additional sensors can be placed at strategic locations to increase the level of security provided. General forms of these two problems are presented in this paper. Next, the minimum exposure path is found by first using the sensing limits of the sensors to compute an approximate “feasible area” of interest, and then using a grid within this feasible area to search for the minimum exposure path in a systematic manner. Two algorithms are presented for the minimum exposure path problem, and an additional subsequently executed algorithm is proposed for sensor deployment. The proposed algorithms are shown to require significantly lower computational complexity than previous methods, with the fastest proposed algorithm requiring O(n2.5) time, as compared to O(mn3) for a traditional grid-based search method, where n is the number of sensors, m is the number of obstacles, and certain assumptions are made on the parameter values.

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“…Tarnaris et al [44] used GA, PSO, grid-based PSO, and a Voronoi-based PSO method to maximize area coverage and area k-coverage. Bonnah et al [45] proposed a combination of the computed minimal exposed path and PSO algorithm using the ratio of covered to uncovered grids as a fitness function. Li and Liu [46] proposed an optimization algorithm for monitoring area coverage based on controlling the node position, which can rapidly improve the coverage effect.…”

Section: Introductionmentioning

confidence: 99%

Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Almotairi,

Badr,

Abdul Salam

et al. 2023

Mathematics

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Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

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Coverage Maximization in Wireless Sensor Networks Using Minimal Exposure Path and Particle Swarm Optimization

Cited by 23 publications

References 22 publications

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

Algorithms for Finding Vulnerabilities and Deploying Additional Sensors in a Region with Obstacles

Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

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