By Topic

A Distributed and Energy Efficient Algorithm for Data Collection in Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sharafkandi, S. ; Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA ; Du, D.H.C. ; Razavi, A.

In wireless sensor networks, collection of raw sensor data at a base station provides the flexibility to perform offline detailed analysis on the data which may not be possible with innetwork data aggregation. However, lossless data collection consumes considerable amount of energy for communication while sensors usually have limited energy. In this paper, we propose a Distributed and Energy efficient algorithm for Collection of Raw data in sensor networks called DECOR. DECOR exploits spatial correlation to reduce the communication energy in sensor networks with highly correlated data. In our approach, at each neighborhood, one sensor shares its raw data as a reference with the rest of sensors without any suppression or compression. Other sensors use this reference data to compress their observations by representing them in the forms of mutual differences. In a highly correlated network, transmission of reference data consumes significantly more energy than transmission of compressed data. Thus, we first attempt to minimize the number of reference transmissions. Then, we try to minimize the size of mutual differences. We derive analytical lower bounds for both these phases and based on our theoretical results, we propose a two-step distributed data collection algorithm which reduces the communication energy significantly compared to existing methods. In addition, we modify our algorithm for lossy communication channels and we evaluate its performance through simulation.

Published in:

Parallel Processing Workshops (ICPPW), 2010 39th International Conference on

Date of Conference:

13-16 Sept. 2010