NeTS-NOSS: Adaptivity in Sensor Networks for Optimized Distributed Sensing and Signal Processing

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This project aims to develop an adaptive sensor network architecture that enables the efficient, large-scale, long-term, low-cost, on-demand monitoring of a variety of physical phenomena with high fidelity. The core theme is that distributed signal processing and data assimilation of sensor data, as well as network management and monitoring, should be performed inside the sensor network in order to reduce energy consumption and global communication needs, leading to dramatically increased sensor lifetimes and much higher fidelity in the tracking of the physical phenomena of interest. The goal is to develop a flexible, self-monitoring architecture for this type of in-network processing and sensor networking that exploits adaptivity to significantly improve the network's efficiency, robustness, and usefulness. Two kinds of adaptivity are considered: (1) data adaptivity, where the network topology is adapted to align communications with the natural data flows; and (2) resource adaptivity, where the network topology is adapted based on computational, battery, or bandwidth resources. The expected results include the development of adaptive communication protocols and routing topology, the development of network management tools for sensor communication performance monitoring and inference, and for sensor distribution monitoring, as well as the experimental deployment of the adaptive sensor network architecture in a small-scale testbed of sensor nodes on the Rice University campus. Results will be disseminated through technical reports posted on the project web page, through papers presented at professional meetings, as well as through journal publications.