Integrating machine learning in ad hoc routing: A wireless adaptive routing protocol

The nodes in a wireless ad hoc network act as routers in a self-configuring network without infrastructure. An application running on the nodes in the ad hoc network may require that intermediate nodes act as routers, receiving and forwarding data packets to other nodes to overcome the limitations of noise, router congestion and limited transmission power. In existing routing protocols, the ‘self-configuring’ aspects of network construction have generally been limited to the construction of routes that minimize the number of intermediate nodes on a route while ignoring the effects that the resulting traffic has on the overall communication capacity of the network. This paper presents a context-aware routing metric that factors the effects of environmental noise and router congestion into a single time-based metric, and further presents a new cross-layer routing protocol, called Warp-5 (Wireless Adaptive Routing Protocol, Version 5), that uses the new metric to make better routing decisions in heterogeneous network systems. Simulation results for Warp-5 are presented and compared to the existing, well-known AODV (Ad hoc On-Demand Distance Vector) routing protocol and the reinforcement-learning based routing protocol, Q-routing. The results show Warp-5 to be superior to shortest path routing protocols and Q-routing for preventing router congestion and packet loss due to noise. Copyright © 2011 John Wiley & Sons, Ltd. This paper presents a cross-layer predictor for route selection in wireless ad hoc networks that combines the effects of noise and router congestion into a single time-based routing metric based on statistical estimation from recent experience. A new cross-layer, adaptive routing protocol, called Warp-5, uses this new routing metric to make better routing decisions in noisy or congested wireless ad hoc networks.