Designing Robust Wireless Ad Hoc Network Routing with Reinforcement Learning for Dynamic Environments

Wireless ad hoc networking (WANETs) is known to be very useful to ambience the communication in numerous dynamic and infrastructure less environment similar to disaster, military and IoT based applications. Nevertheless, the development of optimistic and stable routing planes for WANETs remains a tricky issue because of the following things: Specifically, this research explores the application of RL with an aim of designing context-aware routing protocols that enable WANETs to operate optimally and in a very reliable manner in emerging complex scenarios. This paper builds on a proposed framework that introduces RL agents placed within network nodes to learn other topologies and adapt and make the correct routing in real-time. Candidate KPIs such as the packet delivery ratio, end-to-end delay and energy consumption are then used to assess the performance of the RL based routing protocol. Comparative simulations reveal that the proposed solution is superior to existing solutions including AODV-OD and DSR protocols regarding scalability as well as stability when tested under continually different environmental conditions. Through checking ahead of the time and reducing the occurrence of connection link failures, the system guarantees continued connectivity as well as maximized use of the available resources. This work also revealed that reinforcement learning could substantially transform the approach to routing in WANETs so as to produce a new generation of communication networks, more robust and adaptive to diverse and difficult conditions.