|Baier, Patrick; Dürr, Frank; Rothermel, Kurt: TOMP: Opportunistic Traffic Offloading Using Movement Predictions.
In: Proceedings of the 37th IEEE Conference on Local Computer Networks (LCN).
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1-8, english.
Clearwater: IEEE Computer Society, October 2012.
Article in Proceedings (Conference Paper).
|C.2 (Computer-Communication Networks)
Recent forecasts predict that the amount of cellular data traffic will significantly increase within the next few years. The reason for this trend is on the one hand the high growth rate of mobile Internet users and on the other hand the growing popularity of high bandwidth streaming applications. Given the fact that cellular networks (e.g. UMTS) have only limited capacity, the existing network infrastructure will soon reach its limits. As a result, the concept of traffic offloading attracts more and more attention in research since it aims at the reduction of cellular traffic by shifting it to local-area networks like Wifi. Within the last few years, some first approaches for automatically offloading cellular traffic were proposed. These approaches either assume the wide availability of publicly accessible Wifi networks or knowledge about social relations of mobile users. However, these assumptions are usually not fulfilled. To face this issue, we developed the TOMP system. TOMP implements a system to distribute data from the infrastructure to a set of mobile devices by partly shifting traffic from the cellular network to the level of inter-device communication. In contrast to the prevailing approaches, TOMP does not rely on open Wifi networks and only uses information about the position and speed of mobile device. By using predictions about the future movement of mobile users, TOMP determines devices that are most suitable targets for traffic offloading. In this paper we show by simulation that TOMP can save up to 40% of cellular messages in comparison to a typical cellular network.
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|University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
|July 9, 2012