CAREER: Addressing Data and Energy Management Challenges in Hierarchical Sensor Networks

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This project takes a fresh look at challenges in sensor networks in light of recent technology trends and experiences in pilot deployments. Technology trends indicate that the capacities of flash memories will continue to rise while their costs and energy consumption continue to plummet. This will make it possible to equip sensor nodes with energy-efficient, high-capacity NAND flash memory storage. Pilot deployments have shown that scalable sensor network architectures will be hierarchical, and comprise several hundreds of resource-constrained sensors as well as several tens of resource-rich sensor proxies. This motivates the need to develop novel methods to exploit resources at proxies while respecting constraints at sensors. This project has two contributions. The first contribution is a system for storage and retrieval of archival sensor data. This research includes the design, prototyping and evaluation of archival storage subsystems for sensor nodes, algorithms to enable efficient access of large distributed archival sensor data, and compression techniques for efficiently retrieving such data. Second, this project proposes an uncertainty-driven energy management architecture that unifies energy optimization across sensing, communication, routing, data processing and query processing tasks. This research uses prediction models and uncertainty as fundamental building blocks and builds a spectrum of energy-optimized services over this foundation. The project will have broad impact across data-intensive sensor network applications in science and engineering, as well as on education across the Five College consortium. The results of this project including publications, software and hardware prototypes will be made freely available to the research community.