Open Access Research Article

A Hybrid Model for Accurate Energy Analysis of WSN Nodes

MuhammadMahtab Alam*, Olivier Berder, Daniel Menard, Thomas Anger and Olivier Sentieys

Author Affiliations

IRISA, University of Rennes 1, 6 rue de Kerampont, BP80518 22300 Lannion, Cedex, France

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EURASIP Journal on Embedded Systems 2011, 2011:307079 doi:10.1155/2011/307079


The electronic version of this article is the complete one and can be found online at: http://jes.eurasipjournals.com/content/2011/1/307079


Received:10 June 2010
Revisions received:30 October 2010
Accepted:10 January 2011
Published:24 January 2011

© 2011 Muhammad Mahtab Alam et al.

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.

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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