A Hybrid Model for Accurate Energy Analysis of WSN Nodes

  • MuhammadMahtab Alam1Email author,

    Affiliated with

    • Olivier Berder1,

      Affiliated with

      • Daniel Menard1,

        Affiliated with

        • Thomas Anger1 and

          Affiliated with

          • Olivier Sentieys1

            Affiliated with

            EURASIP Journal on Embedded Systems20112011:307079

            DOI: 10.1155/2011/307079

            Received: 10 June 2010

            Accepted: 10 January 2011

            Published: 24 January 2011

            Abstract

            Energy modeling is an important issue for designing and dimensioning low power wireless sensor networks (WSN). In order to help the developers to optimize the energy spent by WSN nodes, a pragmatic and precise hybrid energy model is proposed. This model considers different scenarios that occur during the communication and evaluates their energy consumption based on software profiling as well as the hardware components power profiles. The proposed model is a combination of analytical derivations and real-time measurements. Firstly, the analytical model provides a global view of various elements of the link and MAC layers and shows their impact on the energy consumption. Secondly, the real-time measurements provide an accurate estimate of the power consumption of the software as well as the hardware platform. These experiments are particularly useful to understand the MAC layer mechanisms, such as wake-up or data collisions for the preamble sampling category, and the energy wasted by collisions is evaluated. The presented model is validated under a specific setup with three different test cases. The results verify that the relative error is between 1 and 8%.

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            Authors’ Affiliations

            (1)
            IRISA, University of Rennes 1

            Copyright

            © Muhammad Mahtab Alam et al. 2011

            This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.