Environment-Aware Rewards Optimized Deep-Q-Learning for Cluster Head Selection in MANET

Abstract: It is obvious that MANETs are dynamic; as a result, network performance declines as the network size grows. Such a problem can be mitigated by implementing clustering. Clustering improves wireless network scalability while decreasing network overhead. In the MANET context, mobile nodes are clustered to reduce processing complexity. A cluster is a group of divided nodes. In a MANET, clustering separates a collection of mobile nodes into virtual logical groupings based on specific criteria. Each cluster comprises a cluster head referred to as CH, cluster members, and cluster gateway; all play distinct functions in the cluster during data transfer in a MANET. Cluster head selection and cluster creation are the two stages of node clustering. Energy state, node degree, distance, trust level, and node mobility are considered when determining the cluster heads’ score values. The cluster leader is chosen from among the nodes having the highest score to maintain the maximum cluster size and enhance cluster stability. In the present work, cluster head selection is implemented using Reward optimized DQN-based algorithm. Appropriate rewards are selected by timely environmental surrounding awareness information. The algorithm develops an ideal strategy for CH’s selection by continuously learning the network state through forwarding packets and feedback from packets. The RoDQL clustering algorithm simulation ensured that the number of nodes in each cluster was balanced. The cluster head will access each node’s connection information, allowing the malicious nodes involved in the Worm Hole attacks to be discovered and destroyed during routing. Better network energy consumption and routing overhead ensured the effectiveness of the proposed algorithm.

Keywords: MANET, Clustering, DQN, Security, Energy.