SAMPLE: An On-Demand Probabilistic Routing Protocol for Ad-hoc Networks

Abstract

Existing on-demand ad hoc routing protocols assume an idealised wireless network in which all links in the network are either on or off and where all functioning links are equally good. Such a model interprets the fraction of packets that are dropped due to contention or interference as broken links, which can in turn lead to increased routing traffic and radio contention. As an alternative to the traditional hop-count metric, this paper presents a new metric for capturing the cost of a route based on a statisical model of network links. To investigage the impact of using this cost metric, we present a probabilistic routing protocol. SAMPLE, inspired by reinforcement learning techniques. Different scenario-based performance evaluations of the protocol in NS-2 are presented. In comparisons with AODV and DSR, SAMPLE exhibits improved performance in both lossy and congested wireless networks.