Adaptive Energy Management for Incremental Deployment of Heterogeneous Wireless Sensors

  • Authors:

  • Ioannis Chatzigiannakis;Athanasios Kinalis;Sotiris Nikoletseas

  • Affiliations:

  • Research Academic Computer Technology Institute, P.O. Box 1382, N. Kazantzaki Str., 26500, Patras, Greece and University of Patras, Department of Computer Engineering and Informatics, P.O. Box 138 ...;Research Academic Computer Technology Institute, P.O. Box 1382, N. Kazantzaki Str., 26500, Patras, Greece and University of Patras, Department of Computer Engineering and Informatics, P.O. Box 138 ...;Research Academic Computer Technology Institute, P.O. Box 1382, N. Kazantzaki Str., 26500, Patras, Greece and University of Patras, Department of Computer Engineering and Informatics, P.O. Box 138 ...

  • Venue:
  • Theory of Computing Systems
  • Year:
  • 2007

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Abstract

We introduce a new modelling assumption for wireless sensor networks, that of node redeployment (addition of sensor devices during protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under these modelling assumptions, we design, implement and evaluate a new power conservation scheme for efficient data propagation. Our scheme is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards improved operation choices. The scheme is simple, distributed and does not require exchange of control messages between nodes. Implementing our protocol in software we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of the network simulator ns-2. We focus on highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-offs between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known $\mathsf{Directed Diffusion}$ propagation protocol) and good trade-offs achieved. Furthermore, the redeployment of additional sensors during network evolution and/or the heterogeneous deployment of sensors, drastically improve (when compared to “equal total power” simultaneous deployment of identical sensors at the start) the protocol performance (i.e. the success rate increases up to four times while reducing energy dissipation and, interestingly, keeping latency low).