Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks

TL;DR

This paper models the tradeoff between classification accuracy and battery lifetime in wireless sensor networks by integrating Fisher discriminant analysis with CSMA protocol dynamics, revealing how communication parameters affect learning performance.

Contribution

It introduces a novel model linking sensor network communication protocols with classification accuracy and battery life, extending prior work by incorporating CSMA dynamics.

Findings

Accuracy varies non-monotonically with back-off rate.

Tradeoff identified between training duration and classification accuracy.

Battery lifetime impacts and is impacted by communication and learning parameters.

Abstract

Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA), the classification accuracy of which has been previously studied in the context of wireless sensor networks. Previous work, however, does not take into account the communication protocol or battery lifetime of the sensor networks; in this paper we extend the existing studies by proposing a model that captures the relationship between battery lifetime and classification accuracy. In order to do so we combine the FDA with a model that captures the dynamics of the Carrier-Sense Multiple-Access (CSMA) algorithm, the random-access algorithm used to regulate communications in sensor networks. This allows us to study the interaction between the classification accuracy, battery lifetime and effort put towards learning, as well as the impact of the back-off rates of CSMA on the accuracy. We characterize the tradeoff between the length of the training stage and accuracy, and show that accuracy is non-monotone in the back-off rate due to changes in the training sample size and overfitting.