---
type: Scholarly Article
title: "ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing"
description: Reinforcement learning assigns primary and backup copies of real-time tasks to fog nodes, balancing communication delay against the workload on each device.
resource: https://doi.org/10.1109/JIOT.2023.3240007
tags:
  - reinforcement learning
  - fog computing
  - real-time systems
  - fault tolerance
  - resource allocation
  - internet of things
  - reliability
generated:
  by: process:roozbeh.nl/astro-build
  at: 2026-09-06T00:00:00Z
status: stable
sources:
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    resource: https://api.crossref.org/works/10.1109/JIOT.2023.3240007
    title: Crossref metadata record
  - id: publisher
    resource: https://doi.org/10.1109/JIOT.2023.3240007
    title: Publisher record (IEEE)
  - id: pdf
    resource: https://roozbeh.nl/papers/ReLIEF.pdf
    title: Author manuscript (PDF)
doi: 10.1109/JIOT.2023.3240007
authors:
  - name: Roozbeh Siyadatzadeh
    orcid: 0000-0002-2258-4110
  - name: Fatemeh Mehrafrooz
    orcid: 0000-0001-5844-3793
  - name: Mohsen Ansari
    orcid: 0000-0002-4670-8608
  - name: Bardia Safaei
    orcid: 0000-0001-9504-8637
  - name: Muhammad Shafique
    orcid: 0000-0002-2607-8135
  - name: Jörg Henkel
    orcid: 0000-0001-9602-2922
  - name: Alireza Ejlali
    orcid: 0000-0002-5661-3629
year: 2023
published: 2023-06-15
venue: IEEE Internet of Things Journal
venue_type: journal
publisher: IEEE
volume: "10"
issue: "12"
pages: 10752-10763
web_page: https://roozbeh.nl/publications/relief-rl-task-assignment-fog/
pdf: https://roozbeh.nl/papers/ReLIEF.pdf
pdf_version: author
bibtex: https://roozbeh.nl/publications/relief-rl-task-assignment-fog.bib
citation_count: 52
citation_count_source: Semantic Scholar Graph API, 2026-09-02
---

# ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing

Roozbeh Siyadatzadeh, Fatemeh Mehrafrooz, Mohsen Ansari, Bardia Safaei, Muhammad Shafique, Jörg Henkel, Alireza Ejlali. IEEE Internet of Things Journal, 2023, pp. 10752-10763. DOI: [10.1109/JIOT.2023.3240007](https://doi.org/10.1109/JIOT.2023.3240007).

# Abstract

Due to the real-time requirements in several IoT applications, fog computing has emerged to overcome the long latency and other constraints of cloud computing. Due to the high probability of packet loss, energy limitation of IoT devices, and the external disturbances that may frequently occur on the fog infrastructure, the timing constraints of real-time tasks may be compromised. Therefore, the reliability of executing real-time tasks has always been a significant challenge in fog computing. In addition to the correct execution of the tasks, it is also important to execute them before their deadlines according to their real-time classification. State-of-the-art methods generally focus on the delay or functionality of tasks in fog computing systems. However, those methods do not widely focus on the reliability of tasks with real-time constraints in dynamic environments. In this paper, a novel primary backup task assignment strategy based on machine learning (ReLIEF) is proposed to improve the reliability of fog-based IoT systems. To identify suitable nodes for the execution of the primary and backup tasks, ReLIEF employs a Reinforcement Learning (RL) approach, which has an outstanding performance in dynamic environments by establishing a balance between communication delay and workload on each fog device. Based on the simulations, our newly proposed technique has been able to reduce the amount of task dropping rate by up to 84% against the state-of-the-art. Moreover, it is capable of balancing the workload distribution while increasing the reliability of the system by nearly 72% compared with its counterparts.

# Key result

Reduced task dropping by up to 84% and improved system reliability by nearly 72% in simulation against state-of-the-art strategies.

# Cite

R. Siyadatzadeh et al., "ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing," IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10752-10763, 2023, doi: 10.1109/JIOT.2023.3240007.

```bibtex
@article{siyadatzadeh2023relief,
  author    = {Siyadatzadeh, Roozbeh and Mehrafrooz, Fatemeh and Ansari, Mohsen and Safaei, Bardia and Shafique, Muhammad and Henkel, Jörg and Ejlali, Alireza},
  title     = {{ReLIEF}: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing},
  journal   = {IEEE Internet of Things Journal},
  volume    = {10},
  number    = {12},
  year      = {2023},
  pages     = {10752--10763},
  publisher = {IEEE},
  doi       = {10.1109/JIOT.2023.3240007},
  url       = {https://doi.org/10.1109/JIOT.2023.3240007},
}
```

# Files

* [Web page](https://roozbeh.nl/publications/relief-rl-task-assignment-fog/)
* [Author manuscript (PDF)](https://roozbeh.nl/papers/ReLIEF.pdf)
* [AI-generated audio overview (NotebookLM), 5 min](https://roozbeh.nl/audio/ReLIEF.m4a)
* [AI-generated video overview (NotebookLM), 7 min](https://roozbeh.nl/videos/ReLIEF.mp4)

# Related

* [Reinforcement Learning for Reliable Real-Time Fog Systems](../notes/rl-realtime.md)
* [My Journey from Bushehr to Leiden](../notes/academic-journey.md)

Author record: [Roozbeh Siyadatzadeh](../person/roozbeh-siyadatzadeh.md).