Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

Sun, Xi; Zhang, Yiming; Xu, Ran; Chen, Ke

doi:10.1016/j.amc.2014.10.091

Cited by 23 publications

(11 citation statements)

References 27 publications

(31 reference statements)

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“…As the number of sensor nodes increases, the coverage of WSNs increases significantly. Our Mobile-Coverage adjustment scheme has better performance than the competitive methods in [16], [17], [13], [51]. As the number of iterations continuously increases, the coverage of our method gradually increases as well.…”

Section: Methodsmentioning

confidence: 95%

“…The positions of sensor nodes can also be adjusted via priori calculation which satisfies a certain geometric condition. Regarding the bio-geography model, previous researchers gave some cases, such as GA (Genetic Algorithm) [16], and ACA (Ant Colony Algorithm) [17], which allowed sensor nodes to adjust their positions by simulating biological habits.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Huang

Liu

et al. 2020

IEEE Access

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In this paper, a novel Mobile-Coverage scheme is proposed to improve the coverage and lifetime of hybrid sensor networks, and thus the target region can be completely covered with minimum number of mobile sensor nodes and the energy consumption can be reduced in hybrid sensor networks at the same time. Our scheme is applied to a hybrid network comprising of both static and mobile sensor nodes, which are randomly deployed in a rectangle area. We derived the minimum number of sensor nodes as well as the mobile nodes required to cover the target region completely. Compared to the existing works, the minimum number of mobile sensor nodes required in this paper is significantly smaller, which implies that the movement distance and energy consumption of mobile nodes in our scheme is reduced. Moreover, we proved that the construction of our scheme based on the Virtual Force and Voronoi Graph (VFVG) is correct, and then derived the optimal trade-off between the coverage of the target region and energy consumption of mobile nodes. Our scheme can achieve the maximum coverage which is 98.7% by minimum number of mobile sensor nodes. Meanwhile, a connectivity rate of 76.84% can also be achieved by our connectivity evaluation method. Finally, a new topology construction algorithm-Topology Construction and Maintenance based on Trust (TCMT) is created to further increase the lifetime of the sensor network. We also evaluated the performance of our scheme in terms of coverage and lifetime, and carried out extensive experiments to compare the coverage and energy consumption in our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated. INDEX TERMS Topology construction and maintain, Minimum numbers of mobile sensor, Voronoi graph, Virtual force, TCMT.

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Section: Methodsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Huang

Liu

et al. 2020

IEEE Access

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“…Also, many other researchers have proposed other state-of-the-art metaheuristic algorithms, such as particle swarm optimization (PSO) [51][52][53][54][55][56], cuckoo search (CS) [57][58][59][60][61], probability-based incremental learning (PBIL) [62], differential evolution (DE) [63][64][65][66], evolutionary strategy (ES) [67,68], monarch butterfly optimization (MBO) [20], firefly algorithm (FA) [69][70][71][72], earthworm optimization algorithm (EWA) [73], genetic algorithms (GAs) [74][75][76], ant colony optimization (ACO) [77][78][79], krill herd (KH) [37,80,81], invasive weed optimization [82][83][84], stud GA (SGA) [85], biogeography-based optimization (BBO) [86,87], harmony search (HS) [88][89][90], and bat algorithm (BA) [91,92], among others Besides benchmark evaluations [93,…”

Section: Related Workmentioning

confidence: 99%

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

et al. 2019

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Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.

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“…Xuemei Sun, et al [3] proposed a paper on Optimization deployment of wireless sensor networks based on culture-ant colony algorithm. The objectives they framed: To deploy sensor nodes to cover all monitored points.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

Balasubramanian¹,

Govindasamy²

2019

EAI Endorsed Trans IoT

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INTRODUCTION: Target based WSN faces coverage issue in which many targets could not be efficiently covered by static deployed sensors. OBJECTIVES: This paper covers the issue of coverage problems by deploying the sensors to cover all the targets with minimized sensors in number. METHODS: This paper proposes a Binary based Monkey King Evolutionary Algorithm for solving target based WSN problem, the proposed model consist a Binary method for converting the continuous values into binary form to solve the choice of potential position to place the sensors. RESULTS: The proposed algorithm is evaluated in a 50x50 grid and 100x100 grid to track the performance and the performance of the proposed is compared with GA and PSO. CONCLUSION: This paper utilized the MKE algorithm for improving the efficiency of the target coverage problem in WSN. It mainly focused on a single objective-based solution providing for small scale problems. From the simulation results, it is provided that the proposed MKE algorithm obtained 1.86 % of the F-value, which is higher than the other optimization algorithms such as GA and PSO.

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Optimization deployment of wireless sensor networks based on culture–ant colony algorithm

Cited by 23 publications

References 27 publications

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

A Novel Mobile-Coverage Scheme for Hybrid Sensor Networks

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Binary Monkey-King Evolutionary Algorithm for single objective target based WSN

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