Scalable Data Gathering In Wireless Sensor NetworksSeptember 29, 2006
Professor Bhaskar Krishnamachari, Electrical Engineering Systems, University of Southern California
We will examine three interesting theoretical research problems pertaining to the energy efficient collection of information from very large scale wireless sensor networks. The first pertains to joint routing and compression, where we will see that while the optimum strategy depends dynamically on the level of correlation in the environment, there exists a simple correlation-unaware near-optimal strategy that is in fact asymptotically optimal. In the second part, we will discover some fundamental scaling laws for data-centric storage and querying mechanisms in the form of conditions under which arbitrarily large sensor networks can be sustained with bounded per-node energy requirements. Finally, we will investigate how the performance of random walk-based queries can be drastically improved in heterogenous networks.
Bhaskar Krishnamachari is an Assistant Professor in the Dept. of Electrical Engineering-Systems at USC, where he holds the Philip and Cayley MacDonald Early Career Chair. His research focuses on the analysis and design of efficient data gathering and self-configuration algorithms for wireless sensor networks. He received the 2004 NSF CAREER award and the USC Viterbi School of Engineering's outstanding junior faculty award in 2005. He serves on the editorial boards of the Ad Hoc Networks journal, the ACM Mobile Computing and Communications Review and the EURASIP Journal on Wireless Communications and Networking and as the Sensor Networks Vice Chair for IEEE ICDCS . He is the author of a textbook titled "Networking Wireless Sensors", published by Cambridge University Press.