Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

Abstract

SummaryMobile ad‐hoc network (MANET) is a category of ad‐hoc network that can be reconfigurable its network. MANETS are self‐organized networks, that can use the wireless links to connect various networks via mobile nodes: but it consumes more energy and it also has routing problems. This is the major drawback of being connected with the MANET technology. Therefore, this study proposes a new protocol as deep Q‐learning network optimized with chaotic bat swarm optimization algorithm (CBS)‐based optimized link state routing (OLSR) (CBS‐OLSR) for MANET. This protocol reduces MANET energy usage and adopts OLSR multi‐point relay (MPR) technology. MANET's OLSR and the CBS algorithm utilize a similar method to locate the best optimum path from source to destination node. By embedding the new improved deep Q‐learning and OLSR algorithms, both are used for optimizing the MPR sets selection, it can efficiently diminish the energy consumption in the network topology, but automatically increase the lifespan of the network. It also enhances the package delivery ratio and decreases end‐to‐end delay. The experimental outcomes prove that the proposed protocol is reliable and proficient that is appropriate for numerous MANET applications.