Average Error Probability in Wireless Sensor Networks with Imperfect Sensing and Communication for Different Decision Rules

TL;DR

This paper develops a framework to evaluate decision error probabilities in wireless sensor networks considering sensing and communication errors, analyzing how different decision rules perform under various system parameters.

Contribution

It provides closed-form equations for error probability based on system parameters and compares decision rules like OR, AND, K-OUT-OF-N, and MAJORITY in different scenarios.

Findings

AND rule is better for rare events with few sensors.

MAJORITY rule outperforms others with many sensors and frequent events.

Error probability for MAJORITY decreases with more sensors but is sensitive to channel errors.

Abstract

This paper presents a framework to evaluate the probability that a decision error event occurs in wireless sensor networks, including sensing and communication errors. We consider a scenario where sensors need to identify whether a given event has occurred based on its periodic, noisy, observations of a given signal. Such information about the signal needs to be sent to a fusion center that decides about the actual state at that specific observation time. The communication links -- single- or multi-hop -- are modeled as binary symmetric channels, which may have different error probabilities. The decision at the fusion center is based on OR, AND, K-OUT-OF-N and MAJORITY Boolean operations on the received signals associated to individual sensor observations. We derive closed-form equations for the average decision error probability as a function of the system parameters (e.g. number of sensors and hops) and the input signal characterization. Our analyses show the best decision rule is closely related to the frequency that the observed events occur and the number of sensors. In our numerical example, we show that the AND rule outperforms MAJORITY if such an event is rare and there is only a handful number of sensors. Conversely, if there is a large number of sensors or more evenly distributed event occurrences, the MAJORITY is the best choice. We further show that, while the error probability using the MAJORITY rule asymptotically goes to 0 with increasing number of sensors, it is also more susceptible to higher channel error probabilities.