The functional correctness and the performance of smart environment applications can be hampered by faults. Fault tolerance solutions aim to achieve graceful performance degradation in the presence of faults, ideally without leading to application failures. This is a reactive approach and, by itself, gives little flexibility and time for preventing potential failures. We argue that the key step in achieving high dependability is to predict faults before they occur. We propose a proactive fault prevention framework, which predicts potential low-level hardware, software and network faults and tries to prevent them via dynamic adaptation. Many statistical fault prediction algorithms have been proposed in the literature. In this paper, we evaluate and compare the performances of two fault prediction models, namely, multiple linear regression, and artificial neural networks by using them to predict the remaining useful life of a battery-powered wireless sensor network node. The results show that the proposed framework will provide better control over performance degradation of smart environment applications, and will increase reliability and availability, and reduce manual user interventions.

This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system.

Domotics, concerned with the realization of intelligent home environments, is a novel field which can highly benefit from solutions inspired by service-oriented principles to enhance the convenience and security of modern home residents. In this work, we present an architecture for a smart home, starting from the lower device interconnectivity level up to the higher application layers that undertake the load of complex functionalities and provide a number of services to end-users. We claim that in order for smart homes to exhibit a genuinely intelligent behavior, the ability to compute compositions of individual devices automatically and dynamically is paramount. To this end, we incorporate into the architecture a composition component that employs artificial intelligence domain-independent planning to generate compositions at runtime, in a constantly evolving environment. We have implemented a fully working prototype that realizes such an architecture, and have evaluated it both in terms of performance as well as from the end-user point of view. The results of the evaluation show that the service-oriented architectural design and the support for dynamic compositions is quite efficient from the technical point of view, and that the system succeeds in satisfying the expectations and objectives of the users.

Wireless Sensor Network (WSN) deployment experiences show that data collected is prone to be imprecise and faulty due to internal and external influences, such as battery drain, environmental interference, sensor aging. An early detection of such faults is necessary for the effective operation of the sensor network. In this preliminary work, we propose a hybrid approach to the detection of faults and we illustrate its performance on data coming from a real sensor deployment. The proposal is a first step to have a hybrid method towards automated on-line fault detection and classification in context-aware WSNs middleware framework.