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Better Strategies for Data Collection in Sensor Networks

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We consider the problem of efficient data gathering in sensor networks for arbitrary sensor node deployments. The efficiency of the solution is measured by a number of criteria: total energy consumption, total transport capacity, latency and quality of the transmissions. We present a number of different constructions with various tradeoffs between aforementioned parameters. We provide theoretical performance analysis for our approaches, present their distributed implementation and discuss the different aspects of using each. We show that in many cases our output-sensitive approximation solution performs better than the currently known best results for sensor networks. The talk is based on joint research with Jon Crowcroft and Liron Levin.

Bio:Michael Segal finished B.Sc., M.Sc. and Ph.D. degrees in Computer Science from Ben-Gurion University of the Negev in 1994, 1997, and 1999, respectively. During a period of 1999–2000 he held a MITACS National Centre of Excellence Postdoctoral Fellow position in University of British Columbia, Canada. Michael joined the Department of Communication Systems Engineering, Ben-Gurion University, Israel in 2000 where he served as department’s Chairman between 2005–2010 and currently holds a position of Full Professor. He published more than 130 journal and conference papers on topics including algorithms (sequential and distributed), data structures with applications to optimization problems, mobile wireless networks, scheduling and efficient networking.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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