On combining network coding with duty-cycling in flood-based wireless sensor networks

  • Authors:

  • Roja Chandanala;Wei Zhang;Radu Stoleru;Myounggyu Won

  • Affiliations:

  • Department of Computer Science and Engineering, Texas A&M University, United States;Department of Computer Science and Engineering, Texas A&M University, United States;Department of Computer Science and Engineering, Texas A&M University, United States;Department of Computer Science and Engineering, Texas A&M University, United States

  • Venue:
  • Ad Hoc Networks
  • Year:
  • 2013

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Abstract

Network coding and duty-cycling are two major techniques for saving energy in wireless sensor networks. To the best of our knowledge, the idea to combine these two techniques for even more aggressive energy savings, has not been explored. This is not unusual, since these two techniques achieve energy efficiency through conflicting means, e.g., network coding saves energy by exploiting overhearing (i.e., nodes are awake), whereas duty-cycling saves energy by reducing idle listening (i.e., nodes sleep). In this article, we thoroughly investigate if network coding and duty cycling can be used together for more aggressive energy savings in flood-based wireless sensor networks. Our main idea is to exploit the redundancy sometimes present in flooding applications that use network coding, and put a node to sleep (i.e., duty cycle) when a redundant transmission takes place (i.e., the node has already received and successfully decoded a sequence of network-coded packets). We propose a scheme, called DutyCode, in which a multiple access control (MAC) protocol implements packet streaming and allows the network coding-aware application to decide when a node can sleep. We also present an algorithm for deciding the optimal coding scheme for a node to further reduce energy consumption by minimizing redundant packet transmissions. Finally, we propose an adaptive switching technique between DutyCode and an existing duty-cycling MAC protocol. We investigate our proposed solutions analytically and implement them on mote hardware. Our performance evaluation results, obtained from a 42-node indoor testbed, show that our scheme saves 30-46% more energy than network coding-based solutions.