Automatic Failure Recovery for Container-Based IoT Edge Applications
Open accessAbstract
Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario.
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StarlingX [12], IoT recovery frameworks [11], and ZigBee [29] self-healing mechanisms report results from deployed or hardware-based implementations.
“whereas platforms such as StarlingX [12], IoT recovery frameworks [11], and ZigBee [29] self-healing mechanisms report results from deployed or hardware-based implementa- tions.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [11]
IoT device redeployment exceeds 70 s.
“IoT device redeployment exceeds 70 s [11]”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [11]
Existing resilience solutions for the edge are too slow, with cold standby recovery times exceeding minutes.
“Second, at an implementation level, existing resilience solutions for the edge are either too heavyweight (container orchestration requiring significant resources [8,35]), too slow (cold standby with recovery times exceeding minutes [11,16]), or too narrow (sensor-level backups that cannot assume full node functionality [13]).”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [11]
The Introduction and §2.3 discuss fault tolerance and fail-over strategies, but do not describe cold standby or give its recovery time. The paper reports redeployment times exceeding two minutes for its own system, which does not establish the claim about existing cold-standby solutions (searched: cold standby, failover, recovery times). (LLM, conf=0.76)
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- Authors
- Olorunnife, K., Lee, K., & Kua, J.
- Affiliations
- Deakin University (AU)
- Corresponding author
- Olorunnife, Kolade
- Lee, Kevin
- Kua, Jonathan
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-12-06
- Volume
- 10
- Issue
- 23
- Pages
- 3047
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4200285338
- Cited by
- 17
- References
- 52
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 2 · 2023: 1 · 2022: 3
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Own your potential (DEAKIN) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- Internet of Things, edge computing, software failure, Docker containers, remote software updates, reconfiguration, remote management
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