The Sensing Capacity of Sensor Networks

TL;DR

This paper introduces the concept of sensing capacity, establishing fundamental limits for large-scale detection in sensor networks and linking it to communication theory to determine minimal sensor requirements.

Contribution

It defines sensing capacity for sensor networks, derives lower bounds, and highlights its differences from classical detection theory and channel capacity.

Findings

Sensing capacity bounds the minimal number of sensors needed for accurate detection.

Sensor configurations create dependent, non-i.i.d. codewords, affecting capacity.

The approach reveals a deep connection between sensor networks and information theory.

Abstract

This paper demonstrates fundamental limits of sensor networks for detection problems where the number of hypotheses is exponentially large. Such problems characterize many important applications including detection and classification of targets in a geographical area using a network of sensors, and detecting complex substances with a chemical sensor array. We refer to such applications as largescale detection problems. Using the insight that these problems share fundamental similarities with the problem of communicating over a noisy channel, we define a quantity called the sensing capacity and lower bound it for a number of sensor network models. The sensing capacity expression differs significantly from the channel capacity due to the fact that a fixed sensor configuration encodes all states of the environment. As a result, codewords are dependent and non-identically distributed. The sensing capacity provides a bound on the minimal number of sensors required to detect the state of an environment to within a desired accuracy. The results differ significantly from classical detection theory, and provide an ntriguing connection between sensor networks and communications. In addition, we discuss the insight that sensing capacity provides for the problem of sensor selection.