Sleep/wake scheduling for multi-hop sensor networks: Non-convexity and approximation algorithm

  • Authors:
  • Yan Wu;Sonia Fahmy;Ness B. Shroff

  • Affiliations:
  • Microsoft Corporation, 14690 NE 35th St., Apt B203, Bellevue, WA 98007, United States;Department of Computer Science, Purdue University, 305 N. University St., West Lafayette, IN 47907-2107, United States;Department of Electrical & Computer Engineering, 205 Dreese Labs, 2015 Neil Avenue, Columbus, OH 43210, United States

  • Venue:
  • Ad Hoc Networks
  • Year:
  • 2010

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Abstract

We investigate the problem of sleep/wake scheduling for low duty cycle sensor networks. Our work differs from prior work in that we explicitly consider the effect of synchronization error in the design of the sleep/wake scheduling algorithm. In our previous work, we studied sleep/wake scheduling for single hop communication, e.g., intra-cluster communication between a cluster head and cluster members. We showed that there is an inherent trade-off between energy consumption and message delivery performance (defined as the message capture probability). We proposed an optimal sleep/wake scheduling algorithm, which satisfies a given message capture probability threshold with minimum energy consumption. In this work, we consider multi-hop communication. We remove the previous assumption that the capture probability threshold is already given, and study how to decide the per-hop capture probability thresholds to meet the Quality of Services (QoS) requirements of the application. In many sensor network applications, the QoS is decided by the amount of data delivered to the base station(s), i.e., the multi-hop delivery performance. We formulate an optimization problem to set the capture probability threshold at each hop such that the network lifetime is maximized, while the multi-hop delivery performance is guaranteed. The problem turns out to be non-convex and hence cannot be efficiently solved using standard methods. By investigating the unique structure of the problem and using approximation techniques, we obtain a solution that provably achieves at least 0.73 of the optimal performance. Our solution is extremely simple to implement.