Abstract
A wireless sensor network is a potential technique which is most suitable for continuous monitoring applications where the human intervention is not possible. It employs large number of sensor nodes, which will perform various operations like data gathering, transmission and forwarding. An optimal Q-learning based clustering and load balancing technique using improved K-Means algorithm is proposed. It contains two phases namely clustering phase and node balancing phase. The proposed algorithm uses Q-learning technique for deploying sensor nodes in appropriate clusters and cluster head CH election. In the clustering phase, the node will be placed in appropriate clusters based on the computation of the mean values. Once the sensors are placed in an appropriate cluster, then the cluster will be divided into ‘k’ partitions. The node which is having maximum residual energy in each partition will be elected as the partition head PH. In node balancing phase, the number of sensors in each partition will be evenly distributed by considering the area of the cluster and the number of sensors inside the cluster. Among the PHs, the node which is having residual energy to the maximum and also having the minimal distance to the sink is elected as the CH. The residual energy of the CH is monitored periodically. If it falls below the threshold level, then another partition head PH which is having residual energy to the maximum level and possessing minimum distance to the sink node will be elected as CH. The proposed Q-Learning based clustering technique maximize the reward by considering the throughput, end-to-end delay, packet delivery ratio and energy consumption. Finally, the performance of the Q-learning based clustering algorithm is evaluated and compared existing k-means based clustering algorithms. Our results indicate that the proposed method reduces end to end delay by 8.23%, throughput is increased by 2.34%, network lifetime is increased by 3.34%, packet delivery ratio is improved by 1.56%.