| Paper: | SS-4.2 | ||
| Session: | Signal Processing for Wireless Sensor Networks I | ||
| Time: | Wednesday, May 19, 09:50 - 10:10 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Signal Processing for Wireless Sensor Networks | ||
| Title: | DISTRIBUTED SOURCE-CHANNEL CODING FOR WIRELESS SENSOR NETWORKS | ||
| Authors: | Michael Gastpar; University of California, Berkeley | ||
| Abstract: | In this paper, we investigate properties of good coding strategies for a class of wireless sensor networks that could be termed ``monitoring'' networks:Their task is to monitor an underlying physical reality at the highest possible fidelity. Since the sensed signals are often analog, and the communication channels noisy, it will not generally be possible to exactly communicate the sensed signals. Rather, such sensor network scenarios involveboth a compression and a communication problem. It is well known that these two tasks must be addressed jointly for optimal performance, but optimal performance is unknown in general. This problem is addressed from a scaling-law perspective in this paper, i.e., as the number of nodes becomes large. The goal of the paper is to characterize the key properties of coding strategies that achieve the optimum scaling behavior, and hence to identify the scaling-law relevant issues in code design. We first present a lower bound to the cost-distortion tradeoff, and then compare two fundamentally different coding strategies to that lower bound. | ||
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