A Comparison of Predictive Algorithms for Failure Prevention in Smart Environment Applications

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.