Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm

Industrial Wireless Sensor Networks (IWSNs), a novel technique in the field of industrial control, can greatly reduce the cost of measurement and control, as well as improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial areas, IWSNs has high requirements for...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2011 Seventh International Conference on Natural Computation Jg. 4; S. 2199 - 2203
Hauptverfasser: Ling Wang, Xiping Fu, Jiating Fang, Haikuan Wang, Minrui Fei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2011
Schlagworte:
Adaptation models
Adaptive Mutation
Binary Particle Swarm Optimization
Industrial Wireless Sensor Networks
Load modeling
Node Placement
Optimization
Particle swarm optimization
Reliability
Sensors
Wireless sensor networks
ISBN:9781424499502, 142449950X
ISSN:2157-9555
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Industrial Wireless Sensor Networks (IWSNs), a novel technique in the field of industrial control, can greatly reduce the cost of measurement and control, as well as improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial areas, IWSNs has high requirements for reliability, especially for large-scale industry application. As the network architecture has great influences on the performance of IWSNs, this paper discusses the node placement problem in IWSNs. Considering the reliability requirements, the setup cost and energy balance in IWSNs, the node placement model of IWSNs is built and an adaptive mutation probability binary Particle Swarm Optimization algorithm (AMPBPSO) is proposed to solve this model. Experimental results show that AMPBPSO is effective for the optimal node placement in IWSNs with various kinds of field scales and different node densities and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (SGA) in terms of network reliability, load uniformity, total cost and convergence speed.
ISBN:9781424499502
142449950X
ISSN:2157-9555
DOI:10.1109/ICNC.2011.6022417