LAUSR

The development of the IIOT paradigm brings the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems eliciting optimal decisions in real time. Ensuring the accuracy and reliability of intelligent wireless sensing and control systems is crucial towards achieving this goal. In this paper, a novel solution is proposed to denoise data while simultaneously preserving actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts to compute a noise score. The trust level of each data point is then calculated through a correlation measure, to purge spurious data. Extensive experiments on real data-sets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency, facilitating the normal operation of the monitored control systems.