Publication

Abstract : The preeminent challenge when data is compiled from cyber–physical system is the detection and filtering of faulty data. When the storage, processing, and communication of a reasonable amount of data migrate to those mobile and embedded devices, it becomes strenuous to apply conventional powerful algorithms for identifying, eliminating, and tolerating the faulty data because of the resource constraints of these devices. Further, the real-time requirements of CPS applications require CPSs to respond quickly and the aggregation method that is long-established in CPSs makes it challenging to ascertain the value of the original data. Thus, a systematic approach is essential to accomplish the goal of faulty data detection and filtering. In this paper, we present several approaches that we can deploy to detect and filter faulty data efficiently and cost-effectively, to improve the quality of the collected data from a system’s perspective to match the specific requirements of CPSs.