Adaptive Fault Detection exploiting Redundancy with Uncertainties in Space and Time
This work addresses the challenge of fault detection in dynamic, heterogeneous IoT/CPS environments by exploiting implicit redundancy, but the evaluation is limited to a single prototype, making the contribution incremental.
The authors propose an adaptive fault detection method for cyber-physical systems that uses a Prolog/ProbLog knowledge base to model implicit redundancy and generate runtime monitors comparing uncertain, asynchronous, multi-rate, and delayed signals. The approach is demonstrated on a real-world rover prototype, showing effective fault detection and recovery triggering.
The Internet of Things (IoT) connects millions of devices of different cyber-physical systems (CPSs) providing the CPSs additional (implicit) redundancy during runtime. However, the increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. Self-healing is an increasingly popular approach for ensuring resilience, that is, a proper monitoring and recovery, in CPSs. This work encodes and searches an adaptive knowledge base in Prolog/ProbLog that models relations among system variables given that certain implicit redundancy exists in the system. We exploit the redundancy represented in our knowledge base to generate adaptive runtime monitors which compares related signals by considering uncertainties in space and time. This enables the comparison of uncertain, asynchronous, multi-rate and delayed measurements. The monitor is used to trigger the recovery process of a self-healing mechanism. We demonstrate our approach by deploying it in a real-world CPS prototype of a rover whose sensors are susceptible to failure.