# Optimally Self-Healing IoT Choreographies

## TL;DR

This paper presents a novel approach for enhancing the reliability of edge IoT applications through adaptive failure detection and efficient mitigation strategies, addressing key challenges in industrial edge computing.

## Contribution

It introduces a policy-enabled failure detector and an energy-efficient allocation component for self-healing edge IoT systems, advancing reliability management in constrained environments.

## Key findings

- Failure detection parameters can be optimized for different edge scenarios.
- The energy-efficient allocation technique reduces mitigation costs.
- The proposed system improves edge application resilience.

## Abstract

In the industrial Internet of Things domain, applications are moving from the Cloud into the edge, closer to the devices producing and consuming data. This means applications move from the scalable and homogeneous cloud environment into a constrained heterogeneous edge network. Making edge applications reliable enough to fulfill Industrie 4.0 use cases is still an open research challenge. Maintaining operation of an edge system requires advanced management techniques to mitigate the failure of devices. This paper tackles this challenge with a twofold approach: (1) a policy-enabled failure detector that enables adaptable failure detection and (2) an allocation component for the efficient selection of failure mitigation actions. We evaluate the parameters and performance of our failure detection approach and the performance of an energy-efficient allocation technique, and present a vision for a complete system as well as an example use case.