Title: Adaptive aggregation tree transformation for energy-efficient query processing in sensor networks
Authors: Mu-Huan Chiang, Gregory T. Byrd
Addresses: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA. ' Department of Electrical and Computer Engineering, Northm Carolina State University, Raleigh, NC, USA
Abstract: Data aggregation reduces energy consumption by reducing the number of message transmissions in sensor networks. Effective aggregation requires that event messages be routed along common paths. While existing routing protocols provide many ways to construct the aggregation tree, this opportunistic style of aggregation is usually not optimal. The Minimal Steiner Tree (MST) maximises the possible degree of aggregation, but finding such a tree requires global knowledge of the network, which is not practical in sensor networks. In this paper, we propose the Adaptive Aggregation Tree (AAT) to dynamically transform the structure of the routing tree to improve the efficiency of data aggregation. It adapts to changes in the set of source nodes automatically, and approaches the cost savings of MST without explicit maintenance of an infrastructure. The evaluation results show that AAT reduces the communication energy consumption by 23%, compared to shortest-path tree, and by 31%, compared to GPSR.
Keywords: aggregation efficiency; query processing; dynamic route selection; overhearing; wireless sensor networks: WSNs; wireless networks; data aggregation; routing tree.
International Journal of Sensor Networks, 2009 Vol.6 No.1, pp.51 - 64
Available online: 29 Aug 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article