Search

A number of algorithms now exist for using model-based prediction at the sensor node of a wireless sensor network (WSN) to enable a dramatic reduction in transmission rates, and thus save energy at the sensor node. These approaches, however, sometimes reduce the rate so substantially as to make the health state of the network opaque. One solution is to include a regular heartbeat transmission whose receipt or otherwise informs the sink about the health state of the node. However, given that a large period increases the probability that dead nodes go unnoticed at the sink, while a small period likely increases the energy cost of communication, what should be the period of the heartbeat transmission? In this paper, we examine the use of heartbeats in WSN design. We derive a general protocol for optimal and dynamic heartbeat transmission by minimising the Bayes risk, which is the expected cost of missing data from dead nodes plus the energy cost of heartbeat transmissions. Our proposed algorithm is dynamic in the sense that the heartbeat period is updated as time goes on and node failures become more probable. We validate our design experimentally using three real-world datasets, and show a 36% reduction in the total heartbeat operational cost over a heartbeat transmission with a fixed period; the results also highlight the superiority of our algorithm over arbitrarily chosen heartbeat periods in different WSN settings, thus promising significant cost savings in WSN applications.

We propose a supervised linear dimensionality reduction algorithm.The algorithm reduces the dimensionality of data to K1 for the K-class problem.The linearly reduced data is well-suited for Bayesian classification.Experiments on UCI datasets are performed for the proposed and existing algorithms.The applicability of the algorithm to flow meter diagnostics is also demonstrated. Supervised linear dimensionality reduction (LDR) performed prior to classification often improves the accuracy of classification by reducing overfitting and removing multicollinearity. If a Bayes classifier is to be used, then reduction to a dimensionality of K1 is necessary and sufficient to preserve the classification information in the original feature space for the K-class problem. However, most of the existing algorithms provide no optimal dimensionality to which to reduce the data, thus classification information can be lost in the reduced space if K1 dimensions are used. In this paper, we present a novel LDR technique to reduce the dimensionality of the original data to K1, such that it is well-primed for Bayesian classification. This is done by sequentially constructing linear classifiers that minimise the Bayes error via a gradient descent procedure, under an assumption of normality. We experimentally validate the proposed algorithm on 10 UCI datasets. Our algorithm is shown to be superior in terms of the classification accuracy when compared to existing algorithms including LDR based on Fishers criterion and the Chernoff criterion. The applicability of our algorithm is then demonstrated by employing it in diagnosing the health states of 2 ultrasonic flow meters. As with the UCI datasets, the proposed algorithm is found to have superior performance to the existing algorithms, achieving classification accuracies of 99.4% and 97.5% on the two flow meters. Such high classification accuracies on the flow meters promise significant cost benefits in oil and gas operations.

We derive a linear classifier for heteroscedastic linear discriminant analysis.The proposed scheme efficiently minimises the Bayes error for binary classification.A local neighbourhood search is also proposed for non-normal distributions.The proposed schemes are experimentally validated on twelve datasets. Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.