Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Wu, Jie; Yang, Shuhui

doi:10.1016/j.simpat.2006.11.006

Cited by 42 publications

(30 citation statements)

References 18 publications

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“…Most deployment algorithm studies mainly focus on the incremental algorithm node deployment, virtual force algorithm, the deployment algorithm based on grid nodes, probabilistic detection model algorithm, various groups intelligent algorithm and other algorithms [3][4][5][6][7]. Incremental algorithm node deployment was described by Andrew Howard et al in 2002 [3].…”

Section: State Of Artmentioning

confidence: 99%

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Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Ge¹,

Wang²,

Hu³

et al. 2017

Int. J. Onl. Eng.

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Abstract-Incidents could not always be avoided, resulting in significant losses for the state property and people's safety. Effective monitoring of incidents is becoming more and more important. Wireless sensor networks (WSNs) are widely used in environmental monitoring. On the basis of the existing wireless sensor network node deployment model, a network node deployment model was proposed, which was used in the monitoring of unexpected accidents. The model area was divided into the core area and the evacuation area, and different monitoring nodes deployment plans were applied in different areas. The simulation experiment is carried out by MATLAB, and the simulation results of the node coverage are obtained. Results show that the model for the practical application of the node deployment provides an effective deployment plan, as well as offers a strong basis for real-time monitoring and post-accident emergency evacuation.

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Section: State Of Artmentioning

confidence: 99%

“…For a given number of sensors, this algorithm attempts to maximize the sensor field coverage. Probabilistic detection model algorithm was described by Jie Wu et al in 2003 [5]. Jie Wu et al focused on minimizing total moving distance and propose an optimal, but centralized solution, based on the Hungarian method.…”

Section: State Of Artmentioning

confidence: 99%

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Ge¹,

Wang²,

Hu³

et al. 2017

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…After this initial placement, sensor nodes move around and cooperatively and gradually find their best positions within the area of the network [15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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“…A r eview o f recent developments in the field of distributed sensor networks can be f ound i n [ 2]. Recently, O SDP of m oving s ensors was addressed in [ 3] and [ 4]. Deployment pr oblem involving underwater acoustic sensors was analyzed in [5].…”

Section: Introductionmentioning

confidence: 99%

“…S ometime, howe ver, s ensors c an be pl aced a t only certain, discrete points within this region. Even if it is not s o, i t i s c ommonly more c onvenient t o c onsider only a discrete sets of poi nts wi thin t he s urveillance r egion a s possible locations for sensor deployment [1], [4], [7]. In the present paper, this approach was also adopted, with primary purpose to r educe the computational load w hen calculating different joined a nd conditional probabilities.…”

Section: Introductionmentioning

confidence: 99%

Discrete particle swarm optimization algorithm for solving optimal sensor deployment problem

2008

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-This paper addresses the Optimal Sensor Deployment Problem (OSDP). The goal is to maximize the probability of target detection, with simultaneous cost minimization. The problem is solved by the Discrete PSO (DPSO) algorithm, a novel modification of the PSO algorithm, originally presented in the current paper. DPSO is generalpurpose optimizer well suited for conducting search within a discrete search space. Its applicability is not limited to OSDP, it can be used to solve any combinatorial and integer programming problem. The effectiveness of the DPSO in solving OSDP was demonstrated on several examples.

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Optimal movement-assisted sensor deployment and its extensions in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), a good sensor deployment

Cited by 42 publications

References 18 publications

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Discrete particle swarm optimization algorithm for solving optimal sensor deployment problem

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