System design issues in sensor databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
An energy-efficient data-driven power management for wireless sensor networks
Proceedings of the 5th workshop on Data management for sensor networks
SNQL: a query language for sensor network databases
TELE-INFO'08 Proceedings of the 7th WSEAS International Conference on Telecommunications and Informatics
MWM: a map-based world model for wireless sensor networks
Autonomics '08 Proceedings of the 2nd International Conference on Autonomic Computing and Communication Systems
Quality aware query scheduling in wireless sensor networks
Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Distributed wake-up scheduling for data collection in tree-based wireless sensor networks
IEEE Communications Letters
Adaptive holistic scheduling for query processing in sensor networks
Journal of Parallel and Distributed Computing
Reducing data aggregation latency by using partially overlapped channels in sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Adaptive real-time query scheduling for wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
International Journal of Sensor Networks
An efficient algorithm for scheduling sensor data collection through multi-path routing structures
Journal of Network and Computer Applications
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In-network sensor query processing is a cross-layer design paradigm in which networked sensor nodes process data acquisitional queries in collaboration with one another. As power efficiency is still one of the most severe constraints in this paradigm, we propose a distributed, cross-layer scheduling scheme for it. In this scheme, each node employs its MAC, routing, and query layers to negotiate with its parent its timing for transmission and constructs a schedule for its query processing. It then follows the schedule to compute, communicate, and sleep in each query processing cycle. This scheduling reduces wasted listening and receiving as well as the switching between active and sleeping modes. Consequently, it results in 50-60% of power saving on real sensor nodes in our experiments. Additionally, it outperforms two existing scheduling schemes both on schedule construction efficiency and on schedule quality.