A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram

Aziz, Nor Azlina Bt Ab.; Mohemmed, Ammar; Alias, Mohamad Yusoff

doi:10.1109/icnsc.2009.4919346

Cited by 142 publications

(72 citation statements)

References 11 publications

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“…However, the simulation results confirm that the proposed scheme executes perfectly for a sparse sensor network. Similar works can also be found in [15][16][17][18] that proposed energy efficient layout for better coverage and connectivity in the sensor networks through the application of particle swarm optimization.…”

Section: Related Workmentioning

confidence: 66%

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Singh

Lobiyal

2012

Hum. Cent. Comput. Inf. Sci.

176

103

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The wireless sensor networks have long been an attractive field to the researchers and scientists for its ease in deployment and maintenance. In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks. Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network. This paper proposes Particle Swarm Optimization (PSO) approach for generating energy-aware clusters by optimal selection of cluster heads. The PSO eventually reduces the cost of locating optimal position for the head nodes in a cluster. In addition, we have implemented the PSO-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Furthermore, influence of the expected number of packet retransmissions along the estimated path towards the cluster head is also reflected in our proposed energy consumption model. The performance evaluation of our proposed technique is carried out with respect to the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. Finally, the simulation clarifies the effectiveness of our proposed work over its comparatives in terms of network lifetime, average packet transmissions, cluster head selection rounds supported by PSO and average energy consumption.

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Section: Related Workmentioning

confidence: 66%

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Singh

Lobiyal

2012

Hum. Cent. Comput. Inf. Sci.

176

103

View full text Add to dashboard Cite

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“…In the future, we will use various GA operators, which are convenient for 2D problem solution, for example, [12], and aim to improve the qualities of the solutions. We intend to perform sensor deployment more realistically by taking into consideration factors such as mobile sensors [6], weather impact [28], lifetime, and various deployment methods [29][30][31][32] and topographical circumstances, by increasing the parallelization level to enable the computing time to approximate real-time.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

Seo

Yoon

Kim

2016

International Journal of Distributed Sensor Networks

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We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times.

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“…al. [2] proposed a new algorithm to optimize sensor coverage using PSO and Voronoi diagrams. PSO is used to find the optimal deployment of sensors providing the best coverage, while Voronoi diagram is used to evaluate the fitness of the solution.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks

Serik

Kaddour

2015

IFIP Advances in Information and Communication Technology

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Abstract. We discuss in this paper a deployment optimization problem in camera-based wireless sensor networks. In particular, we propose a mathematical model to solve the problem of minimizing the number of cameras required to cover a set of targets with a given level of quality. Since solving this kind of problems with exact methods is computationally expensive, we rather rely on an adapted version of Binary Particle Swarm Optimization (BPSO). Our preliminary results are motivating since we obtain near-optimal solutions in few iterations of the algorithm. We discuss also the relevance of hybrid meta-heuristics and parallel algorithms in this context. Keywords:Camera-based wireless sensor networks, minimum cost deployment, coverage quality, binary particle swarm optimization.

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A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram

Cited by 142 publications

References 11 publications

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks

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