Designing Cognitive Wireless Sensor Networks Using Learning Automata

Adding cognition to the existing Wireless Sensor Networks (WSNs) with a cognitive networking approach brings about many benefits. Cognitive networking deals with using cognition to the entire network protocol stack to achieve stack-wide as well as network-wide performance goals; unlike cognitive radios that apply cognition only at the physical layer to overcome the problem of spectrum scarcity. To the best of our knowledge, almost all of the existing researches on the Cognitive Wireless Sensor Networks (CWSNs) have focused on spectrum allocation and interference reduction which are related to the physical layer optimization. In this paper we discuss the design of optimization algorithms for CWSNs using learning automata. Proposed algorithms select appropriate protocols and carrying out learning automata on the resulting stack in order to maximize the perceived network performance. We test our algorithms using the ns-2.35 simulation platform. Our results show that learning automata approach works well to apply cognition in WSNs. KeywordsCognitive Wireless Sensor Network; Learning Automata