Energy-Efficient Collaborative Query Processing Framework for Mobile Sensing Services

Many emerging context-aware mobile applications involve the execution of continuous queries over sensor data streams generated by a variety of on-board sensors on multiple personal mobile devices (aka smartphones). To reduce the energy-overheads of such large-scale, continuous mobile sensing and query processing, this paper introduces CQP, a collaborative query processing framework that exploits the overlap (in both the sensor sources and the query predicates) across multiple smartphones. The framework automatically identifies the shareable parts of multiple executing queries, and then reduces the overheads of repetitive execution and data transmissions, by having a set of `leader´ mobile nodes execute and disseminate these shareable partial results. To further reduce energy, CQP utilizes lower-energy short-range wireless links (such as Bluetooth) to disseminate such results directly among proximate smartphones. We describe algorithms to support our server-assisted distributed query sharing and optimization strategy. Simulation experiments indicate that this approach can result in 60% reduction in the energy overhead of continuous query processing; when `leader´ selection is dynamically rotated to equitably share the burden, we observe an increase of up to 65% in operational lifetime.