Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks

Hu, Xiao-Min; Zhang, Mengjie; Yu, Yan; Chung, Henry Shu-Hung; Li, Yuanlong; Shi, Yuhui; Luo, Xiliang

doi:10.1109/tevc.2010.2040182

Cited by 117 publications

(90 citation statements)

References 42 publications

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“…A redundant rate represents the density of sensors deployed in the target area. The redundant rate in the 2D ideal plane model is computed as (6) according to [39], where area(Γ) is the area of Γ and N is the number of sensors.…”

Section: Critical Parametersmentioning

confidence: 99%

“…Lai et al [38] propose a GA for maximum disjoint set covers (GAMDSC), which applies a scattering operator to the EA offspring to keep critical sensors from joining the same cover set. Hu et al [39] propose a schedule transition hybridized genetic algorithm (STHGA), which adopts a forward encoding scheme for chromosomes and utilizes redundancy information via designing a series of transition operations. Ant-colony optimization for maximizing the number of connected cover (ACO-MNCC) is proposed in [40], which maximizes the lifetime of heterogeneous WSNs.…”

Section: Introductionmentioning

confidence: 99%

“…The merging of local feasible solution is modeled as a Maximum Weight Perfect Matching (MWPM) problem such that Kuhn-Munkres (KM) algorithm [41][42] is applicable;  A redundant-trend sensor schedule strategy (RTSS) to further improve global search efficiency, which is easier to implement than the existing auxiliary schedule strategies [39], and  A modified fitness index by introducing a coverage inadequate penalty to guide convergence better. The rest of this paper is organized as follows.…”

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confidence: 99%

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Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Zhang¹,

Zhang

Gong³

et al. 2016

IEEE Trans. Evol. Computat.

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Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Transactions on Evolutionary Computation.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/118977/ Abstract-Operating mode scheduling is crucial for the lifetime of wireless sensor networks. However, the growing scale of networks has made such a scheduling problem more and more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn-Munkres parallel genetic algorithm is developed to solve the set cover problem and is applied to lifetime maximization of large-scale wireless sensor networks. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial Kuhn-Munkres algorithm are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy is developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.Index Terms-parallel genetic algorithm, set cover problem, large-scale wireless sensor networks, Kuhn-Munkres algorithm.

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Section: Critical Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Zhang¹,

Zhang

Gong³

et al. 2016

IEEE Trans. Evol. Computat.

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“…They are not sensed or covered by any nodes [18]. The irregularity of the blind zones is analyzed by using the mobile nodes.…”

Section: B Coverage Optimization Strategy Based On Mobile Nodesmentioning

confidence: 99%

Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

Zhu¹,

Fan²,

Wu³

et al. 2016

Int. J. Onl. Eng.

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Abstract-To reduce the blind zone in network coverage, we propose a coverage optimization algorithm of wireless sensor network based on mobile nodes. This algorithm calculates the irregularity of blind zone in network coverage and obtains the minimum approximate numerical solution by utilizing the quantitative relationship between energy consumption of related nodes and the position of the mobile nodes. After determining the optimal relative position of the mobile nodes, the problem of blind zone between the static nodes is addressed. Simulation result shows that the proposed algorithm has high dynamic adaptability and can address the problem of blind zone maximally. Besides increasing the network coverage, the algorithm also reduces the network energy consumption, optimizes network coverage control and exhibits high convergence.Index Terms-mobile node; Wireless Sensor Network; network coverage rate; static nodes; the blind zone.

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“…Death of central point is the end of the system. In contrast to it, decentralized architecture needs powerful autonomous entities on one hand, and [44], fault detection [45], self-con�guration [46] Arti�cial immune network [34] Immune system [47] Misbehavior detection [48], image pattern recognition [49] Genetic algorithm [35] Natural evolution system Dynamic shortest path routing [50], lifetime maximization [51] Cellular automata [29] Life Life like/ game of life Large network simulations [52], area coverage scheme [53] Rendering (computer graphics) [30] Patterns of animal skins, birds feathers, mollusk shells, and bacterial colonies [54] Range-free localization [55] Fractal geometry Clouds, river networks, snow�akes, cauli�ower or broccoli, and systems of blood vessels and pulmonary vessels, ocean waves Antenna designing [56] Communication networks and protocols Epidemiology Cross-layer communication protocol [57,58] [61], target tracking [62] then it also re�ects the behavior of adaptation (�exible to changing environment), self-assembly (unit to be one), selforganizing (interaction for the one), and self-regulation, (keeping the process tunably smooth) [25] as in WSN due to the distributed knowledge, distributed control and scalable properties on the other hand. Although the architecture is application dependent, decentralized system has variety of advantages over the centralized system.…”

Section: � �Rom �Eal To �Rti��ialmentioning

confidence: 99%

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

Jabbar

Iram

Minhas

et al. 2013

International Journal of Distributed Sensor Networks

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is survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. e marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of arti�cial systems' problems has been shaped into an important �eld with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. e process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.

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Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks

Cited by 117 publications

References 42 publications

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

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