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Original Articles

Infuse: A TDMA Based Data Dissemination Protocol for Sensor Networks

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Pages 55-78 | Published online: 23 Feb 2007
 

Reliable dissemination of bulk data is one of the important problems in sensor networks. For example, programming or upgrading the software in sensors at run-time requires reliable dissemination of a new program across the network. In this paper, we present Infuse, a time division multiple access (TDMA) based reliable data dissemination protocol. Infuse takes two input parameters:

i.

the choice of the recovery algorithm (from one of two presented in this paper) to deal with unexpected channel errors (e.g., message corruption, varying signal strength), and

ii.

whether a sensor should listen only to a subset of its neighbors to reduce the amount of active radio time.

Based on these parameters, we obtain four possible versions of Infuse. We compare the performance of these versions to assist a designer in selecting the appropriate version based on the network characteristics. Furthermore, we demonstrate Infuse in the context of network programming.

This work was partially sponsored by NSF CAREER CCR-0092724, DARPA Grant OSURS01-C-1901, ONR Grant N00014-01-1-0744, NSF Equipment Grant EIA-0130724, a grant from Michigan State University.

Notes

This work was partially sponsored by NSF CAREER CCR-0092724, DARPA Grant OSURS01-C-1901, ONR Grant N00014-01-1-0744, NSF Equipment Grant EIA-0130724, a grant from Michigan State University.

1Infuse v.; to cause to be permeated with something (as a principle or quality) that alters usually for the better (infuse the team with confidence. (Source: Merriam Webster Online, http://www.m-w.com/).

3. Crossbow Technology, Inc. Mote in-network programming use reference version 20030315, http://www.xbow.com/Support/Support_pdf_files/Xnp.pdf, 2003.

6. T. Stathopoulos, J. Heidemann, and D. Estrin, “A remote code update mechanism for wireless sensor networks.” Technical Report CENS-TR-30, University of California, Los Angeles, Center for Embedded Networked Computing, November, 2003.

21. D. Ganesan, B. Krishnamachari, A. Woo, D. Culler, D. Estrin, and S. Wicker, “An empirical study of epidemic algorithms in large scale multihop wireless networks,” Technical Report IRB-TR-02-003, Intel Research, March, 2002.

29. V. Naik, A. Arora, P. Sinha, and H. Zhang, “Sprinkler: A reliable and scalable data dissemination service for wireless embedded devices,” May, 2005. ExScal Note Series, ExScal-OSU-EN04-2005-05-11, Department of Computer Science and Engineering, The Ohio State University.

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