The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives
Abstract
The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring ever-increasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the above-mentioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.
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Cites
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Cited by
Modern computing: Vision and challenges
Gill, S. S., Wu, H., Patros, P., Ottaviani, C., Arora, P., Pujol, V. C., Haunschild, D., Parlikad, A. K., Cetinkaya, O., Lutfiyya, H., et al. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100116 · Cited by 197
Self-citationAlsadie, D. (2024) · IEEE Access · doi:10.1109/access.2024.3447097 · Cited by 81
Quantum Computing: Vision and Challenges
Gill, S. S., Cetinkaya, O., Marrone, S., Claudino, D., Haunschild, D., Schlote, L., Wu, H., Ottaviani, C., Liu, X., Machupalli, S. P., et al. (2025) · Quantum Computing · doi:10.1016/b978-0-443-29096-1.00008-8 · Cited by 44
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“Numerous researchers have studied the blockchain-empowered EC-IIoT [7], [8], [9].”
Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review
Maimaitiaili, M., Jiamaliding, Y., Dai, G., Xiao, H., Kuerbanjiang, W., & Yi, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/79788 · Cited by 14
Metadata
- Authors
- Walia, G. K., Kumar, M., & Gill, S. S.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2024
- Volume
- 26
- Issue
- 1
- Pages
- 619-669
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W4389161457
- Cited by
- 279
- References
- 180
- FWCI
- 31.33
- Type
- journal article
- Language
- en
- Open access
- closed
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