Enhanced path prediction for network resource management in wireless LANs

Abstract

Path prediction is currently being considered for use in the context of mobile and wireless computing toward more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels the user enjoys. Such mechanisms are mostly meaningful in infrastructures like wireless LANs. In this article we present a path prediction algorithm that exploits the machine learning algorithm of learning automata. The decision of the learning automaton is driven by the movement patterns of a single user but is also affected by the aggregated patterns demonstrated by all users. Simulations of the algorithm, performed using the realistic mobility pattern generator, show increased prediction accuracy.