Application-driven, energy-efficient communication in wireless sensor networks

  • Authors:

  • Giuseppe Amato;Antonio Caruso;Stefano Chessa

  • Affiliations:

  • Istituto di Scienza e Tecnologie dell'Informazione, Area della Ricerca, CNR, 56124 Pisa, Italy;Dipartimento di Matematica, University of Lecce, Collegio Fiorini, via Arnesano, 73100 Lecce, Italy;Istituto di Scienza e Tecnologie dell'Informazione, Area della Ricerca, CNR, 56124 Pisa, Italy and Computer Science Department, University of Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy

  • Venue:
  • Computer Communications
  • Year:
  • 2009

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Abstract

Several sensor network applications based on data diffusion and data management can determine the communication transfer rate between two sensors beforehand. In this framework, we consider the problem of energy efficient communication among nodes of a wireless sensor network and propose an application-driven approach that minimizes radio activity intervals and prolongs network lifetime. On the basis of possible communication delays we estimate packet arrival intervals at any intermediate hop of a fixed-rate data path. We study a generic strategy of radio activity minimization wherein each node maintains the radio switched on just in the expected packet arrival intervals and guarantees low communication latency. We define a probabilistic model that allows the evaluation of the packet loss probability that results from the reduced radio activity. The model can be used to optimally choose the radio activity intervals that achieve a certain probability of successful packet delivery for a specific radio activity strategy. Relying on the probabilistic model we also define a cost model that estimates the energy consumption of the proposed strategies, under specific settings. We propose three specific strategies and numerically evaluate the associated costs. We finally validate our work with a simulation made with TOSSIM (the Berkeley motes' simulator). The simulation results confirm the validity of the approach and the accuracy of the analytic models.