Geodesic
Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm
Computer Science and Information Systems, Tome 9 (2012) no. 4.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

Industrial Wireless Sensor Networks (IWSNs), a novel technique in industry control, can greatly reduce the cost of measurement and control and improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial applications, the communication reliability of IWSNs has to be guaranteed as the real-time field data need to be transmitted to the control system through IWSNs. Obviously, the network architecture has a significant influence on the performance of IWSNs, and therefore this paper investigates the optimal node placement problem of IWSNs to ensure the network reliability and reduce the cost. To solve this problem, a node placement model of IWSNs is developed and formulized in which the reliability, the setup cost, the maintenance cost and the scalability of the system are taken into account. Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme. After the verification of the model and optimization algorithm on the benchmark problem, the presented AMPBPSO and the optimization model are used to solve various large-scale optimal sensor placement problems. The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (GA) in terms of search accuracy and the convergence speed with the guaranteed network reliability.
Keywords: industrial wireless sensor networks, node placement, binary particle swarm optimization, adaptive mutation 
@article{CSIS_2012_9_4_a10,
     author = {Ling Wang and Wei Ye and Haikuan Wang and Muhammad Ilyas Menhas},
     title = {Optimal {Node} {Placement} of {Industrial} {Wireless} {Sensor} {Networks} {Based} on {Adaptive} {Mutation} {Probability} {Binary} {Particle} {Swarm} {Optimization} {Algorithm}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {9},
     number = {4},
     year = {2012},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2012_9_4_a10/}
}
TY  - JOUR
AU  - Ling Wang
AU  - Wei Ye
AU  - Haikuan Wang
AU  - Muhammad Ilyas Menhas
TI  - Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm
JO  - Computer Science and Information Systems
PY  - 2012
VL  - 9
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2012_9_4_a10/
ID  - CSIS_2012_9_4_a10
ER  - 
%0 Journal Article
%A Ling Wang
%A Wei Ye
%A Haikuan Wang
%A Muhammad Ilyas Menhas
%T Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm
%J Computer Science and Information Systems
%D 2012
%V 9
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2012_9_4_a10/
%F CSIS_2012_9_4_a10
Ling Wang; Wei Ye; Haikuan Wang; Muhammad Ilyas Menhas. Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm. Computer Science and Information Systems, Tome 9 (2012) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2012_9_4_a10/