Abstract
Data aggregations from Sensors to a sink in wireless sensor networks (WSNs) are typically characterized by correlation along the spatial, semantic, and temporal dimensions. Exploiting such correlation when performing data aggregation can result in considerable improvements in the bandwidth and energy performance of WSNs. For the sensors-to-sink data delivery, we first explore two theoretical solutions: the shortest path tree (SPT) and the minimum spanning tree (MST) approaches. To approximate the optimal solution (MST) in case of perfect correlation among data, we propose a new aggregation which combines the minimum dominating set (MDS) with the shortest path tree (SPT) in order to aggregate correlated data. To reduce the redundancy among correlated data and simplify the synchronization among transmission, the proposed aggregation takes two stages: local aggregation among sensors around a node in the MDS and global aggregation among sensors in the MDS. Finally, using discrete event simulations, we show that the proposed aggregation outperforms the SPT and closely approximates the centralized optimal solution, the MST, with less amount of overhead and in a decentralized fashion.
Recommended Citation
S. J. Park and R. Sivakumar, "Energy Efficient Correlated Data Aggregation For Wireless Sensor Networks," International Journal of Distributed Sensor Networks, vol. 4, no. 1, pp. 13 - 27, SAGE Publications, Jan 2008.
The definitive version is available at https://doi.org/10.1080/15501320701774592
Department(s)
Computer Science
Publication Status
Free Access
Keywords and Phrases
Reliable Transport Protocols; Wireless Sensor Networks
International Standard Serial Number (ISSN)
1550-1477; 1550-1329
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 SAGE Publications, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2008
