An Ant Colony Optimization Approach for the Deployment of Reliable Wireless Sensor Networks

Deif, Dina S.; Gadallah, Yasser

doi:10.1109/access.2017.2711484

Cited by 68 publications

(44 citation statements)

References 24 publications

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“…An ACO algorithm joined with a nearby search heuristic is proposed as a course of action. Extensive experimental results uncover the adequacy of the proposed technique in finding high-quality solutions for the hassle [11].…”

Section: Literature Surveymentioning

confidence: 95%

A Detailed Survey on Challenges, Applications and Attacks in Wireless Sensor Network

2017

IJAERD

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Section: Literature Surveymentioning

confidence: 95%

A Detailed Survey on Challenges, Applications and Attacks in Wireless Sensor Network

2017

IJAERD

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“…However, this method does not guarantee sufficient coverage of the ROI, especially when sensors nodes adopt a configuration with small concentrations in specific locations of the service area. Since the coverage performance of a WSN depends on the spatial arrangement of the sensor nodes, techniques to properly distribute sensor nodes had been extensively studied and developed in the last few years; as a result, there are plenty of works on the literature devoted to this subject [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm

Fausto¹,

Cuevas²,

Maciel-Castillo³

et al. 2019

IJCIS

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A B S T R A C TWireless sensor networks (WSNs) involves a set of wireless sensor nodes located within a region of interest (ROI) to acquire and/or transmit specific information from their surroundings. A common problem in the operation of WSNs is sensor coverage, which is related to the distribution of sensor nodes within their ROI. Several approaches have been proposed to solve this problem; however, most of these methods consider a simplified arrangement scheme based on sensor placement over a set of fixed discrete locations defined by a grid. This fact severally limits the ability of these methods to find potentially better solutions as they are conditioned to select a limited number of candidate solutions. In this paper, a real-coded sensor deployment approach based on the Social Spider Optimization (SSO) algorithm is proposed to solve the problem of optimal sensor deployment (OSD) in WSNs. The performance of our proposed approach (referred in this paper as real-coded SSO [R-SSO]) was also compared against other metaheuristics-based methods used in the literature. Experimental results demonstrate its ability to solve the problem of OSD in terms of accuracy and robustness.

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“…[5] PSO Energy and coverage area (by clustering) [6] PSO Coverage area [8] Ant Colony Routing [36] Ant Colony Routing [37] SPEA2 Routing [38] DE, PSO, GA Optimal path [39] Ant Colony Node deployment (target coverage) [40] Combined GA and PSO Node deployment (target coverage) [41] MOSA and NSGA-II Node deployment [42] SPEA-2 and NSGA-II Energy, coverage area [43] NSGA-II and LA Energy, coverage area [44] MOEA/D and NSGA-II Energy, coverage area [45] NSGA-II, MOPSO, H3P Node deployment (target coverage) [46] NSGA-II Node deployment (target coverage), routing where ( , ) is the number of times that the coordinate ( , ) is covered by the node set. Nevertheless, to obtain the proper covered area by the set of nodes, the function ( , ) is computed to verify if the coordinate is covered at least once or if it is not covered at all .…”

Section: Othersmentioning

confidence: 99%

“…Optimal path problem is treated in [38], where DEA is the best solution compared to the classical PSO and GA strategies. Node anchors can be deployed with bioinspired techniques as shown in [39] with Ant Colony. Such scenario is found when RFID readers are deployed to detect tags.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

Céspedes-Mota

Castañón

Martínez-Herrera

et al. 2018

Mathematical Problems in Engineering

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Resource efficiency in wireless ad hoc networks has become a widely studied NP-problem. This problem may be suboptimally solved by heuristic strategies, focusing on several features like the channel capacity, coverage area, and more. In this work, maximizing coverage area and minimizing energy consumption are suboptimally adjusted with the implementation of two of Storn/Price's Multiobjective Differential Evolution (DE) algorithm versions. Additionally, their extended representations with the use of random-parameter into the mutation operator were also evaluated. These versions optimize the initial random distribution of the nodes in different shaped areas, by keeping the connectivity of all the network nodes by using the Prim-Dijkstra algorithm. Moreover, the Hungarian algorithm is applied to find the minimum path distance between the initial and final node positions in order to arrange them at the end of the DE algorithm. A case base is analyzed theoretically to check how DE is able to find suboptimal solutions with certain accuracy. The results here computed show that the inclusion of random-and completion of the algorithm, where the area is pondered with 60% and the energy is pondered with 40%, lead to energy optimization and a total coverage area higher than 90%, by considering the best results on each scenario. Thus, this work shows that the aforementioned strategies are feasible to be applied on this problem with successful results. Finally, these results are compared against two typical bioinspired multiobjective algorithms, where the DE algorithm shows the best tradeoff.

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An Ant Colony Optimization Approach for the Deployment of Reliable Wireless Sensor Networks

Cited by 68 publications

References 24 publications

A Detailed Survey on Challenges, Applications and Attacks in Wireless Sensor Network

A Detailed Survey on Challenges, Applications and Attacks in Wireless Sensor Network

A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm

Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

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