Applied Mathematics & Information Sciences

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Recently, inWireless Sensor Networks (WSNs), several energy efficient event detection algorithms have been proposed that aim to minimize battery storage, energy consumption and to maximize the network lifetime by framing reduced fuzzy rules related to spatio-temporal properties for determining the event. Since sensors are prone to intermittent fault it would result to fault susceptible reading that generates false alarm event, which would lead to lack of conforming the event at a higher confidence rate. Hence, during decision making, the confidence level parameter of the sensor node is integrated by considering the spatio-temporal properties. In this paper, we demonstrate through collaborative techniques by setting hypothesis to confirm the composite event collected from the neighborhood nodes later the intelligent fuzzy decision system evaluates the rules with the composition of novel parameter confident factor, which results in higher event detection accuracy. This work is implemented in MATLAB and simulations are carried out under different network scenarios. The algorithm is evaluated with various metrics such as event detection accuracy, false positive rate, error rate of the event and energy consumption. Based on the results of the simulations, we conclude that our intelligent hypothesis based on the fuzzy decision system outperform than the well-established J48 decision tree classification algorithm

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