Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints

Aitsaadi, Nadjib; Achir, Nadjib; Boussetta, Khaled; Pujolle, Guy

doi:10.1016/j.comnet.2010.07.017

Cited by 54 publications

(38 citation statements)

References 21 publications

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“…In order to calculate attraction-repulsion forces between the sensors based on the particle model of the EM algorithm which is expressed by Algorithm 1, the charge values of each sensor are calculated using (2). (9…”

Section: (8) Calculate Charge Of the Sensorsmentioning

confidence: 99%

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Ozdag

Karcı

2015

International Journal of Distributed Sensor Networks

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The dynamic deployment of sensors in wireless networks significantly affects the performance of the network. However, the efficient application of dynamic deployments which determines the positions of the sensors within the network increases the coverage area of the network. As a result of this, dynamic deployment increases the efficiency of the wireless sensor networks (WSNs). In this paper, dynamic deployment was applied to WSNs which consist of mobile sensors by aiming at increasing the coverage area of the network with electromagnetism-like (EM) algorithm which is a population-based optimization algorithm. A new approach has been improved in calculating the coverage rate of the sensors by using binary detection model so as to carry out the dynamic deployments of sensors and it has been thought to reach realistic results efficiently. Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.

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Section: (8) Calculate Charge Of the Sensorsmentioning

confidence: 99%

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Ozdag

Karcı

2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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“…Consequently, differentiated deployment algorithms should be designed based on the required detection probability thresholds at different locations. Some differentiated strategies [11][12][13][14] will be listed as follows.…”

Section: Related Workmentioning

confidence: 99%

“…In Reference [13], the differentiated deployment problem is formulated as a combinatorial optimization model and then solved based on the Tabu search metaheuristic and the artificial potential field. In the strategy, a required event detection probability is guaranteed at each point.…”

Section: Related Workmentioning

confidence: 99%

Sensor Network Deployment under Distance Uncertainty with Robust Optimization

Qiao¹,

Liu²,

Zhang³

et al. 2015

Int. J. Onl. Eng.

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Abstract-We consider the sensor deployment problem in the context of distance uncertainty. It is characterized by differentiated arrangement of specific detection probability thresholds at different locations. The problem is formulated as an integer linear programming (ILP) model firstly, aiming at optimizing the number of sensors and their locations. Based on the robust discrete optimization methodology, the uncertain model is transformed into an equivalent ILP problem considering distance uncertainty. The proposed approach can control the tradeoff between optimality and robustness by varying the parameters named protection levels. Uniform and non-uniform event detection probabiliy distributions are considered in the experiment. The results show that, as the distance uncertainty increases, the constraint violation can be avoided in the robust model and the robust solution can provide a significant improvement at the expense of a small loss in optimality when compared to the optimal solution of a deterministic scenario.

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“…Aitsaadi et al [10] proposed PFDA algorithm and MODA algorithm based on artificial potential field method, which combines artificial potential field method and Tabu search algorithm to perform complete coverage for the target area. However, the nodes deployment strategies above in initialization stage need to be deployed manually, so it is not applicable for the sensor networks with random deployment [11].…”

Section: Nonuniform Coveragementioning

confidence: 99%

“…So, in this paper, we assume the point whose detection probability does not meet the condition of detection will generate an attractive potential field to the mobile nodes. The potential field function can be given by the following formula [10]:…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

Ying

Yunlong

Zhao

et al. 2015

International Journal of Distributed Sensor Networks

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For a hybrid sensor network, the effective coverage rate can be optimized by adjusting the location of the mobile nodes. For many deployments by APF (artificial potential field), due to the common problem of barrier effect, it is difficult for mobile nodes to diffuse by the weaker attraction when the nodes initially distribute densely in some places. The proposed deployment algorithm PFPSO (Potential Field-Directed Particle Swarm Optimization) can overcome this problem and guide the mobile nodes to the optimal positions. Normally the requirement is different for the effective coverage rate between the hotspot area and the ordinary area. On the basis of PFPSO, NPFPSO (Nonuniform PFPSO) algorithm was also proposed to implement nonuniform coverage according to the importance degree of the monitoring area. Simulation result illustrates that PFPSO algorithm can effectively improve the effective coverage rate of the network, and NPFPSO algorithm can obtain a balanced result of effective coverage rate for both hotspot area and ordinary area.

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Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints

Cited by 54 publications

References 21 publications

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Sensor Network Deployment under Distance Uncertainty with Robust Optimization

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

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