Trusting sensors measurements in a WSN: An approach based on true and group deviation estimation

Abstract

Quality-of-service (QoS) and accuracy are of prime importance in WSN-based monitoring applications, as they may need to report real-time measurements leading to efficient decision making. The tiny sensors are often subject to measurement errors, say noise, and prone to failures and attacks, as their physical characteristics change easily due to environmental abnormality and mechanical shock. Faulty information may induce erroneous decisions, which may significantly impact the performance of the network and its service quality. Thus, the sensors’ need to be calibrated periodically and its data has to be trustworthy in making a good decision. In this paper, we have proposed a trust management framework based on true and group deviation metrics to analyze the accuracy and trustworthiness of the sensors’ data. We have derived an analytical model to calibrate the sensors periodically and to examine the trustworthiness. Our simulation results on testing a real-time fire monitoring system showed that the proposed trust framework is efficient in producing 95% accurate and trusted measurements by limiting the frequency of sensor calibrations to a very low value and by setting a lower boundary of 5% deviation from the true and group value metrics.