Energy efficient clustering and secure routing using reinforcement learning for three-dimensional mobile ad hoc networks

Abstract

Mobile ad hoc network (MANET) comprises a set of wireless nodes that provide communication benefits for users without any access points or centralised control. Owing to the highly dynamic wireless nature, MANET has many challenges such as limited energy, packet loss, and security. Therefore, a special energy efficient and secure routing protocol is of great importance in MANETs. In this study, three-dimensional (3D) MANET is considered where mobile nodes are distributed and moved within 3D space. The energy consumption (EC) and throughput increases in the 3D environment compared to 2D. Initially, cell clustering is performed by node residual energy, distance, and mobility in which a cumulative fitness value is computed using the firefly algorithm (firefly with cyclic randomisation algorithm). Then introduced a novel routing scheme referred to as two-hop relay selection by multi-metric based reinforcement learning algorithm. Multi-metric considered for routing is the following: node delay, node distance, node stability, node mobility, and node degree. Before transmission, data packets are encrypted based on the robust cipher watermarking scheme (watermark generation, embedding, and extraction). An extensive simulation is conducted using NS3.26 under a 3D environment in terms of packet delivery ratio, end-to-end delay, routing packet overhead, EC, and security strength.