<title>Data compression trade-offs in sensor networks</title>

Abstract

This paper first discusses the need for data compression within sensor networks and argues that data compression is a fundamental tool for achieving trade-offs in sensor networks among three important sensor network parameters: energy-efficiency, accuracy, and latency. Next, it discusses how to use Fisher information to design data compression algorithms that address the trade-offs inherent in accomplishing multiple estimation tasks within sensor networks. Results for specific examples demonstrate that such trades can be made using optimization frameworks for the data compression algorithms.