Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks

Abstract

Wireless sensor networks (WSNs) play an important role in various real-time applications such as health monitoring, security application, military applications, etc. However, these networks are deployed in ISM bands where coexistence, interference and frequency overlapping remain the challenging issues to degrade the performance. Current technological advancements facilitate the cognitive radios as a promising technique to mitigate these issues. However, energy consumption degrades the network lifetime and throughput performance which can be addressed by developing the energy aware routing protocol for cognitive radio enabled WSNs. In this work, we introduce Energy aware Q-learning AODV (EAQ-AODV) routing. The proposed EAQ-AODV uses Q-learning based reward mechanism for cluster head selection and AODV enabled routing protocol based on different parameters such as Residual Energy, Common Channel, Number of Hops, Licensed Channel, Communication Range and Trust Factor to establish the routing path. The experimental study shows that the proposed EAQ-AODV routing achieves an improved performance in terms of average end-to-end delay, average energy consumption and network lifetime when compared with the existing techniques.