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Smart Environments (SE) and Internet of Things (IoT) are two concepts that connect consumer electronics (CE) to each other and to the Internet domain. This enables various applications where CE devices work together to achieve goals of their users by communicating over a network. Application failures due to fluctuation of resources and environmental factors must be prevented, even though it is a challenge...
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...
The functionality 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 propose a proactive fault-prevention...
Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus...
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...
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. We focus on identifying data fault types and their causes. In particular, we propose...
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