# Adaptive Fault Detection exploiting Redundancy with Uncertainties in Space and Time

## TL;DR

This paper presents an adaptive fault detection method for IoT-based cyber-physical systems that leverages implicit redundancy and uncertainties to improve fault detection and self-healing in complex, heterogeneous environments.

## Contribution

It introduces a novel approach using an adaptive knowledge base in Prolog/ProbLog to model system relations and generate runtime monitors that handle uncertainties and asynchrony.

## Key findings

- Effective fault detection in a rover prototype with sensor failures
- Adaptive monitors handle uncertainties and asynchronous data
- Improved system resilience through self-healing mechanisms

## Abstract

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.