MANET location prediction using machine learning algorithms

Authors:
Fraser Cadger;Kevin Curran;Jose Santos;Sandra Moffett
Affiliations:
Intelligent Systems Research Centre, School of Computing and Intelligent Systems Faculty of Computing and Engineering, University of Ulster, Northern Ireland;Intelligent Systems Research Centre, School of Computing and Intelligent Systems Faculty of Computing and Engineering, University of Ulster, Northern Ireland;Intelligent Systems Research Centre, School of Computing and Intelligent Systems Faculty of Computing and Engineering, University of Ulster, Northern Ireland;Intelligent Systems Research Centre, School of Computing and Intelligent Systems Faculty of Computing and Engineering, University of Ulster, Northern Ireland
Venue:
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
Year:
2012

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Abstract

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. Three popular machine learning techniques have been implemented in MATLAB and tested using data obtained from a variety of mobile simulations in the ns-2 simulator. This paper presents the results of these experiments with the aim of guiding and encouraging development of location-predictive MANET applications.