Effect of mobility models on reinforcement learning based routing algorithm applied for scalable ad hoc network environment

摘要

Mobile Ad Hoc Network faces the greatest challenge for better performances in terms of mobility udcharacterization. The mobility of nodes and their underlying mobility models have a profound effect on the udperformances of routing protocols which are central to the design of ad hoc networks. Most of the udtraditional routing algorithms proposed for ad hoc networks do not scale well when the traffic variation udincreases drastically. To model a solution to this problem we consider a reinforcement learning based udrouting algorithm for ad hoc network known as SAMPLE. Most the scalability issues for ad hoc network udperformance investigation have not considered the group mobility of nodes. In this paper we model udrealistic group vehicular mobility model and analyze the robustness of a reinforcement learning based udrouting algorithm under scalable conditions