An overview of data aggregation architecture for real-time tracking with sensor networks

Tian He, Lin Gu, Liqian Luo, Ting Yan, John A. Stankovic, Sang H. Son

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Since sensor nodes normally have limited resources in terms of energy, bandwidth and computation capability, efficiency is a key design goal in sensor network research. As one of techniques to achieve efficiency, data aggregation has been extensively investigated in recent literature. Previous research on data aggregation has demonstrated its effectiveness in reducing traffic, easing congestion and decreasing the energy consumption. However few are actually designed for a real-world application and implemented in a running system. This paper describes our design and implementation of a physical tracking system, using an aggressive data aggregation architecture as one of building blocks. This architecture can be generally applied to other sensor systems, where communication efficiency is a paramount concern and networking resources are limited.

Original languageEnglish (US)
Title of host publication20th International Parallel and Distributed Processing Symposium, IPDPS 2006
PublisherIEEE Computer Society
ISBN (Print)1424400546, 9781424400546
DOIs
StatePublished - Jan 1 2006
Event20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006 - Rhodes Island, Greece
Duration: Apr 25 2006Apr 29 2006

Publication series

Name20th International Parallel and Distributed Processing Symposium, IPDPS 2006
Volume2006

Other

Other20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006
CountryGreece
CityRhodes Island
Period4/25/064/29/06

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    He, T., Gu, L., Luo, L., Yan, T., Stankovic, J. A., & Son, S. H. (2006). An overview of data aggregation architecture for real-time tracking with sensor networks. In 20th International Parallel and Distributed Processing Symposium, IPDPS 2006 [1639413] (20th International Parallel and Distributed Processing Symposium, IPDPS 2006; Vol. 2006). IEEE Computer Society. https://doi.org/10.1109/IPDPS.2006.1639413