Learning Based Route Management in Mobile Ad-Hoc Networks

Abstract

Ad hoc networks are mobile wireless networks where each node is acting as a router. The existing routing protocols such as Destination sequences distance vector, Optimized list state routing protocols, Ad hoc on demand routing protocol, dynamic source routing are optimized versions of distance vector or link state routing protocols.  Reinforcement Learning is new method evolved recently which is learning from interaction with an environment. Q Learning which is based on Reinforcement learning that learns from the delayed reinforcements and becomes more popular in areas of networking. Q Learning is applied  to the routing algorithms where the routing tables in the distance vector algorithms are replaced by the estimation tables called as Q values. These Q values are based on the link delay. In this paper, various optimization techniques over Q routing are described in detail with their algorithms.