SENSORS: Models and Algorithms for Collaborative Computation in Sensor Networks

University Of Southern California, Los Angeles CA

Investigators

Abstract

Majority of the research results in the field of wireless sensor networking have been substantiated mostly through simulations and empirical measurements, as is common in traditional networking research. For efficient design, in terms of latency, energy, robustness, etc., models that abstract node hardware and the network characteristics are needed for systematic algorithm design and analysis. The proposed work will demonstrate that models of computation for sensor networks (from a parallel and distributed systems' perspective) will create a modular, layered paradigm for application development. The intellectual merit of this research is the development of computation models and robust, adaptive, energy-efficient collaborative algorithms for computation and communication in wireless sensor networks. High-level models will allow designers to make informed decisions regarding energy and time tradeoffs, and robustness at the node and network level - eliminating most of the ad-hoc-ness in application design for sensor networks. The benefits of our approach will be demonstrated on two classes of end-to-end applications. Highly optimized computation and communication kernels for information dissemination in sensor networks, and distributed image processing will be developed. The broader impact of this work is in understanding, modeling, and exploiting sensor networks as a computing substrate - not just a loose federation of nodes equipped with sensors, processors, and radios. We expect this to lead towards a new discipline for programming sensor networks by providing the application developer with high-level technology-independent 'knobs' for analysis and performance optimization. A direct educational impact of the proposed activity will be the introduction of new curriculum in academia to impart knowledge on algorithm design for sensor networks.