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An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

View Article: PubMed Central

ABSTRACT

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

No MeSH data available.


Dynamic deployment after (a) initial random placement and after the optimization of the (b) VF algorithm, (c) PSO, (d) VFPSO, and (e) VFCPSO.
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f5-sensors-07-00354: Dynamic deployment after (a) initial random placement and after the optimization of the (b) VF algorithm, (c) PSO, (d) VFPSO, and (e) VFCPSO.

Mentions: Fig. 5 illustrates the simulation results. The initial locations of stationary sensor nodes are shown in Fig. 5(a), where the effective coverage area is 68.15%. The final results carried out by VF, PSO, VFPSO and VFCPSO are shown in Fig. 5(b), (c), (d) and (e), respectively, where the grey level presents the detection probability of each point. In this independent operate, the effective coverage of the deployment carried out by VF, PSO, VFPSO and VFCPSO are 71.99%, 89.06%, 91.68%, 95.98%.


An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment
Dynamic deployment after (a) initial random placement and after the optimization of the (b) VF algorithm, (c) PSO, (d) VFPSO, and (e) VFCPSO.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756725&req=5

f5-sensors-07-00354: Dynamic deployment after (a) initial random placement and after the optimization of the (b) VF algorithm, (c) PSO, (d) VFPSO, and (e) VFCPSO.
Mentions: Fig. 5 illustrates the simulation results. The initial locations of stationary sensor nodes are shown in Fig. 5(a), where the effective coverage area is 68.15%. The final results carried out by VF, PSO, VFPSO and VFCPSO are shown in Fig. 5(b), (c), (d) and (e), respectively, where the grey level presents the detection probability of each point. In this independent operate, the effective coverage of the deployment carried out by VF, PSO, VFPSO and VFCPSO are 71.99%, 89.06%, 91.68%, 95.98%.

View Article: PubMed Central

ABSTRACT

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

No MeSH data available.