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D. Ratasich, M. Platzer, R. Grosu, E. Bartocci:
"Adaptive Fault Detection exploiting Redundancy with Uncertainties in Space and Time";
Talk: 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Umeå, Sweden; 2019-06-16 - 2019-06-20; in: "2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)", IEEE, (2019), 23 - 32.

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 compare 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.

fault detection, self-healing, cyber-physical sys- tems, redundancy model, observation model

http://dx.doi.org/10.1109/SASO.2019.00013