Achieving end-to-end goals of WSN using Weighted Cognitive Maps

Authors:
Amr El Mougy;Mohamed Ibnkahla
Affiliations:
Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L3N6, Canada;Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L3N6, Canada
Venue:
LCN '12 Proceedings of the 2012 IEEE 37th Conference on Local Computer Networks (LCN 2012)
Year:
2012

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A cognitive framework for WSN based on weighted cognitive maps and Q-learning

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Abstract

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.