University of Ulsan
In mobile ad-hoc networks where users are potentially highly mobile, knowledge of future location and movement can be of great value to routing protocols. To date, most work regarding location prediction has been focused on infrastructure networks and consists of performing classification on a discrete range of cells or access points. Such techniques are unsuitable for infrastructure-free MANETs and although classification algorithms can be used for specific, known areas they are not general or flexible enough for all real-world environments. Unlike previous work, this paper focuses on regression-based machine learning algorithms that are able to predict coordinates as continuous variables.