Hierarchical power management in disruption tolerant networks with traffic-aware optimization

  • Authors:

  • Hyewon Jun;Mostafa H. Ammar;Mark D. Corner;Ellen W. Zegura

  • Affiliations:

  • Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;University of Massachusetts, Amherst, MA;Georgia Institute of Technology, Atlanta, GA

  • Venue:
  • Proceedings of the 2006 SIGCOMM workshop on Challenged networks
  • Year:
  • 2006

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Abstract

Recent efforts in Disruption Tolerant Networks (DTNs) have shown that mobility can be a powerful means for delivering messages in highly-challenged environments. DTNs are wireless mobile networks that are particularly useful in sparse environments where the density of nodes is insufficient to support direct end-to-end communication. Unfortunately, many mobility scenarios depend on untethered devices with limited energy supplies. Without careful management depleted energy supplies will degrade network connectivity and counteract the robustness gained by mobility. A primary concern is the energy consumed by wireless communication, and in particular the energy consumed in searching for other nodes to communicate with. In this architecture, energy can be conserved by using the low-power radio to discover communication opportunities with other nodes and waking the high-power radio to undertake the data transmission. We develop a generalized power management framework for controlling the wakeup intervals of the two radios. In addition, we show how to incorporate knowledge of the traffic load, and we devise approximation algorithms to control the sleep/wake-up cycling to provide maximum energy conservation while discovering enough communication opportunities to handle that load. We evaluate our schemes through simulation and compare them against single radio architectures, and against algorithms that do not incorporate information about the load. Our results show that the generalized power management mechanism achieves better energy efficiency in discovering communication opportunities than mechanisms relying on only one radio. Also, when traffic load can be predicted, our approximation algorithm could reduce energy consumption from 73% to 93% compared with the case without power management. Finally, our results show that the statistical information about traffic load and network connectivity allows us to devise an efficient algorithm even for the one-radio architecture, so energy can be saved almost equivalently to the two radio architecture.