IQL-OCDA: An intelligent Q-learning-based for optimal clustering and data-aggregation for wireless sensor networks

Abstract

Wireless sensor networks (WSNs) can suffer from low battery life due to the energy consumption of the routing protocol. Small sensor nodes are often difficult to recharge after deployment. In a WSN, data aggregation is generally used to reduce or eliminate data redundancy between nodes in order to save energy. In the proposed algorithm, sensor nodes are deployed in appropriate clusters and cluster heads are elected using Q-learning techniques. Nodes are clustered based on the mean values computed during the clustering phase. Lastly, a performance evaluation and comparison of existing clustering algorithms are performed based on Intelligent Q-learning. The proposed IQL-OCDA model reduces end-to-end delay by 10.11%, increases throughput by 4.15%, and increases network lifetime by 5.1%.