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37th Annual IEEE Conference on Local Computer Networks
Achieving end-to-end goals of WSN using Weighted Cognitive Maps
Clearwater Beach, FL, USA USA
October 22-October 25
ISBN: 978-1-4673-1565-4
Amr El Mougy, Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L3N6, Canada
Mohamed Ibnkahla, Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L3N6, Canada
In this paper, a novel cognitive engine for Wireless Sensor Networks (WSN) is proposed in order to achieve its end-to-end goals. This engine is designed using the tool known as Weighted Cognitive Maps (WCM). WCMs have the advantage of being able to consider multiple conflicting objectives and constraints with low complexity. Their inference properties also allow them to resolve complex network interactions using simple mathematical operations. Methods for designing the WCM system are illustrated. The performance of the proposed system is evaluated using computer simulations. Simulation results show that the WCM system outperforms its existing counterparts in metrics of network lifetime, throughput, and PLR.
Index Terms:
Wireless sensor networks,Throughput,Protocols,Routing,Simulation,Quality of service,Optimization,weighted cognitive map,Cognitive,sensor
Citation:
Amr El Mougy, Mohamed Ibnkahla, "Achieving end-to-end goals of WSN using Weighted Cognitive Maps," lcn, pp.328-331, 37th Annual IEEE Conference on Local Computer Networks, 2012
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