A Mobility-Prediction-Based Relay Deployment Framework for Conserving Power in MANETs

  • Authors:
  • Aravindhan Venkateswaran;Venkatesh Sarangan;Thomas F. La Porta;Raj Acharya

  • Affiliations:
  • Qualcomm Inc.;Oklahoma State University, Stllwater;Pennsylvania State University, University Park;Pennsylvania State University, University Park

  • Venue:
  • IEEE Transactions on Mobile Computing
  • Year:
  • 2009

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Abstract

There has been a growing interest in designing mobile systems consisting of special relay nodes whose mobility can be controlled by the underlying network. In this paper, we consider the design of a heterogeneous mobile ad hoc network (MANET) consisting of two kinds of mobile nodes—traditional nodes with limited energy and a few controllable mobile relay nodes with relatively abundant energy resources. We propose a novel relay deployment framework that utilizes mobility prediction and works in tandem with the underlying MANET routing protocol to optimally define the movement of the relay nodes. We present two instances of the relay deployment problem, together with the solutions, to achieve different goals. Instance 1, termed Min-Total, aims to minimize the total energy consumed across all the traditional nodes during data transmission, while instance 2, termed Min-Max, aims to minimize the maximum energy consumed by a traditional node during data transmission. Our solutions also enable the prioritization of individual nodes in the network based on residual energy profiles and contextual significance. We perform an extensive simulation study to understand the trade-offs involved in deploying an increasing fraction of such relay nodes in the network. We also investigate the performance of the proposed framework under different mobility prediction schemes. Results indicate that even when the relay nodes constitute a small fraction of the total nodes in the network, the proposed framework results in significant energy savings. Further, we observed that while both the schemes have their potential advantages, the differences between the two optimization schemes are clearly highlighted in a sparse network.