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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.


An example of VF based dynamic deployment in WSNs: (a) initialization and (b) dynamic deployment result after 1000 iterations, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes.
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f1-sensors-07-00354: An example of VF based dynamic deployment in WSNs: (a) initialization and (b) dynamic deployment result after 1000 iterations, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes.

Mentions: Although the VF algorithm is proven to perform well in WSNs with only mobile sensor nodes [6, 7, 8], its performance may be deteriorated in the context of WSNs with stationary sensor nodes and mobile sensor nodes. Figure 1 illustrates an example of dynamic deployment after 1000 iterations of the VF algorithm, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes. Obviously, most of the mobile sensor nodes are badly confined in the boundary of stationary sensor nodes, which imply that the virtual force exerted by stationary sensor nodes will confine the movement of mobile sensor nodes.


An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment
An example of VF based dynamic deployment in WSNs: (a) initialization and (b) dynamic deployment result after 1000 iterations, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-07-00354: An example of VF based dynamic deployment in WSNs: (a) initialization and (b) dynamic deployment result after 1000 iterations, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes.
Mentions: Although the VF algorithm is proven to perform well in WSNs with only mobile sensor nodes [6, 7, 8], its performance may be deteriorated in the context of WSNs with stationary sensor nodes and mobile sensor nodes. Figure 1 illustrates an example of dynamic deployment after 1000 iterations of the VF algorithm, where the arrows present the orientations and magnitudes of virtual forces between wireless sensor nodes. Obviously, most of the mobile sensor nodes are badly confined in the boundary of stationary sensor nodes, which imply that the virtual force exerted by stationary sensor nodes will confine the movement of mobile sensor nodes.

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.