Efficient Power Utilization in Multi-radio Wireless Ad Hoc Networks

  • Roy Friedman
  • Alex Kogan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5923)


Short-range wireless communication capabilities enable the creation of ad hoc networks between devices such as smart-phones or sensors, spanning, e.g., an entire high-school or a small university campus. This paper is motivated by the proliferation of devices equipped with multiple such capabilities, e.g., Blue-Tooth (BT) and WiFi for smart-phones, or ZigBee and WiFi for sensors. Yet, each of these interfaces has significantly different, and, to a large extent complementing, characteristics in terms of energy efficiency, transmission range, and bandwidth. Consequently, a viable ad hoc network composed of such devices must be able to utilize the combination of these capabilities in a clever way. For example, BT is an order of magnitude more power efficient than WiFi, but its transmission range is also an order of magnitude shorter. Hence, one would want to shut down as many WiFi transmitters as possible, while still ensuring overall network connectivity. Moreover, for latency and network capacity reasons, in addition to pure connectivity, a desired property of such a solution is to keep the number of BT hops traversed by each transmission below a given threshold k.

This paper addresses this issue by introducing the novel k-Weighted Connected Dominating Set (kWCDS) problem and providing a formal definition for it. A distributed algorithm with a proven approximation ratio is presented, followed by a heuristic protocol. While the heuristic protocol has no formally proven approximation ratio, it behaves better than the first protocol in many practical network densities. Beyond that, a tradeoff between communication overhead and the quality of the resulting kWCDS emerges. The paper includes simulation results that explore the performance of the two protocols.


Span Tree Transmission Range Minimal Span Tree Power Utilization Short Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Roy Friedman
    • 1
  • Alex Kogan
    • 1
  1. 1.Department of Computer ScienceTechnion 

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