Results in statistical discriminant analysis: a review of the former Soviet union literature
Journal of Multivariate Analysis
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Network adiabatic theorem: an efficient randomized protocol for contention resolution
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Detection of Gauss-Markov random fields with nearest-neighbor dependency
IEEE Transactions on Information Theory
Self-organization properties of CSMA/CA systems and their consequences on fairness
IEEE Transactions on Information Theory
On the stability of flow-aware CSMA
Performance Evaluation
A distributed CSMA algorithm for throughput and utility maximization in wireless networks
IEEE/ACM Transactions on Networking (TON)
Spatial fairness in linear random-access networks
Performance Evaluation
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Performance analysis of the IEEE 802.11 distributed coordination function
IEEE Journal on Selected Areas in Communications
Collision-minimizing CSMA and its applications to wireless sensor networks
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
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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 learning 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; in this paper we extend existing studies by proposing a model that captures the relationship between battery lifetime and classification accuracy. 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.