ECOQUAD:
Energy-Conserving Quality-Aware Data Collection in Wireless Sensor Networks*
[Abstract]
Recent advances in signal processing, micro-electronics, and wireless communications have enabled the deployment of large-scale sensor networks for many applications such as habitat and wildlife monitoring. A wireless sensor network is typically constructed of a large number of sensor nodes which are battery powered. Thus, energy efficiency is a critical consideration in the design of such a network. While most existing studies have focused on reducing data communication in providing exact answers to user queries, this proposed research exploits the trade-off between data quality and communication cost to improve energy efficiency. We consider approximate queries with precision guarantees, which suffice the purpose of many sensor applications. Both one-shot queries and long-running queries will be studied. We propose a two-tier data collection architecture in support of various types of one-shot queries. Under this architecture, strategies for query processing and the issue of optimal precision setting will be examined. We will then extend our research to the more complex and challenging long-running aggregate queries, where the precision constraint of data aggregation will be partitioned and distributed to the sensor nodes involved. Precision allocation and distribution techniques for this unique problem will be developed. Finally, we plan to develop a testbed and a simulator to demonstrate, experiment, and evaluate the proposed techniques.
[Objectives]
The specific goals of this project are:
to develop a two-tier data collection architecture to support various types of one-shot approximate queries and investigate the query processing and precision setting issues under the architecture;
to develop novel precision allocation techniques for long-running approximate queries;
to develop a testbed and a simulator to demonstrate, experiment, and evaluate the proposed techniques.
[Relevant Publications]
* This project is supported by the Research Grants Council (RGC), Hong Kong SAR, China under Project No. HKBU211505. For further information, please contact Jianliang Xu.