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Authors:
| Md. Hafizur Rahman | Researcher, HafizLab, Bangladesh |
Index Terms:
Edge computing, Fog computing, Internet of Things (IoT), serverless edge, data offloadingAbstract:
The rapid proliferation of Internet of Things (IoT) devices has created unprecedented demands on network bandwidth, data processing, and latency-sensitive applications. Traditional cloud-centric architectures are increasingly inadequate for supporting the real-time requirements and scalability challenges posed by IoT ecosystems. Edge computing has emerged as a transformative paradigm, enabling computation and storage resources to be placed closer to data sources and end-users. This comprehensive review explores the evolution of edge computing, focusing on its integration with IoT from architectural, operational, and application perspectives. Key enabling technologies, design architectures, and deployment models are analyzed, highlighting their roles in enhancing performance, security, and privacy. The review also discusses prominent use cases across industries, summarizes major challenges—such as interoperability, resource management, and standardization—and outlines future research directions. Ultimately, this paper provides an in-depth perspective on how edge computing is shaping the future of IoT-driven smart environments.Conclusion:
Edge computing has rapidly emerged as a transformative paradigm for the Internet of Things (IoT), addressing the limitations of traditional cloud-centric architectures in terms of latency, bandwidth, scalability, privacy, and reliability. By decentralizing computation and storage, edge computing enables real-time processing, context-aware intelligence, and enhanced security at or near the source of data generation. This review has provided a comprehensive overview of the fundamentals, drivers, architectures, applications, and use cases of edge computing in IoT environments. We have discussed the critical challenges faced in deploying and managing edge-enabled IoT systems, including heterogeneity, resource constraints, security, service orchestration, and standardization. Despite these challenges, ongoing research and technological advancements are paving the way for scalable, robust, and intelligent edge-IoT ecosystems. Emerging trends such as AI at the edge, next-generation networking, sustainable architectures, and enhanced security frameworks are expected to further expand the potential of edge computing in diverse domains. In conclusion, edge computing stands as a key enabler for the next generation of IoT systems, empowering new applications and services that demand low latency, high reliability, and context-awareness. Continued interdisciplinary research, industry collaboration, and the development of open standards will be crucial to overcoming existing barriers and realizing the full promise of edge-enabled IoT for society and industry a like.License:
Articles published in OAJEA are licensed under a Creative Commons Attribution 4.0 International License.Cite This Paper:
Md Hafizur Rahman “A Comprehensive Review of Edge Computing: A Perspective of IoT”, Open Access Journal on Engineering Applications (OAJEA), Volume No. 01, Issue No. 01, Page 29-37, July, 2025. https://oajea.hafizlab.com/article/01-01-004Reference:
[1] Lubogo, Isaac Christopher. The Internet of Things: Connecting a Smarter World-A Lesson for Uganda. 2023.
[2] Mewada, A., Singh, N., Ansari, M.A., Singh Yadav, A. (Eds.). Applications of Blockchain Technology (1st ed.). Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003545620
[3] Sachdeva, Kumud; Aggarwal, Shruti. A Hybrid Approach for Neural Network in Pattern Storage. Fusion: Practice and Applications, 2021, pp. 43-49. https://doi.org/10.54216/FPA.060201
[4] Kaswan, Kuldeep Singh; Jagjit Singh Dhatterwal; Vivek Jaglan; Balamurugan Balusamy; Kiran Sood. Enabling Technologies for Smart Fog Computing. IET, 2023.
[5] Buyya, Rajkumar; Srirama, Satish Narayana (Eds.). Fog and Edge Computing: Principles and Paradigms. John Wiley & Sons, 2019.
[6] Rusty Flint. Edge Computing Benefits: Edge vs cloud computing. Quantum Zeitgeist. Available: https://quantumzeitgeist.com/edge-computing-benefits-edge-vs-cloud-computing [Accessed May 2025]
[7] Ahmed, M.; Haskell-Dowland, P. (Eds.). Secure Edge Computing: Applications, Techniques and Challenges (1st ed.). CRC Press, 2021. https://doi.org/10.1201/9781003028635
[8] Ferreira, Rafael Ehrich Pontes. Computer Vision and Machine Learning Applications for Dairy Farming. The University of Wisconsin-Madison, 2024.
[9] Rani, S. (Ed.). Emerging Technologies and the Application of WSN and IoT: Smart Surveillance, Public Security, and Safety Challenges (1st ed.). CRC Press, 2024. https://doi.org/10.1201/9781003438205
[10] Pei, Yongsheng; Peng, Zhangyou; Wang, Zhenling; Wang, Haojia. Energy-Efficient Mobile Edge Computing: Three-Tier Computing under Heterogeneous Networks. Wireless Communications and Mobile Computing, 2020, 17 pages. https://doi.org/10.1155/2020/6098786
[11] Chloe. Hybrid Cloud Microservices: Balancing On-Premise And Cloud Deployments Seamlessly. Moments Log. Available: https://www.momentslog.com/development/infra/hybrid-cloud-microservices-balancing-on-premise-and-cloud-deployments-seamlessly [Accessed May 2025]
[12] Brogi, A.; Forti, S. QoS-Aware Deployment of IoT Applications Through the Fog. IEEE Internet of Things Journal, 4(5), 1185-1192, Oct. 2017. doi:10.1109/JIOT.2017.2701408
[13] V. Jha, A.; Appasani, B. (Eds.). Cyber Physical System 2.0: Communication and Computational Technologies (1st ed.). CRC Press, 2024. https://doi.org/10.1201/9781003559993
[14] Dhanalakshmi, M.; Tamilarasi, K.; Saravanan, S.; Sujatha, G.; Boopathi, Sampath. Fog computing-based framework and solutions for intelligent systems: Enabling autonomy in vehicles. In Computational Intelligence for Green Cloud Computing and Digital Waste Management, pp. 330-356. IGI Global Scientific Publishing, 2024.
[15] Kannadhasan, S.; Nagarajan, R.; Karthick, A.; Chinnaiyan, V.K. (Eds.). Technological Applications for Smart Sensors: Intelligent Applications for Real-Time Strategies (1st ed.). Apple Academic Press, 2025. https://doi.org/10.1201/9781003610717
[16] Rani, S.; Bhambri, P.; Kumar, S.; Pareek, P.K.; Elngar, A.A. (Eds.). AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications (1st ed.). CRC Press, 2024. https://doi.org/10.1201/9781003395416
[17] Awotunde, J.B.; Muduli, K.; Brahma, B. (Eds.). Computational Intelligence for Analysis of Trends in Industry 4.0 and 5.0 (1st ed.). Auerbach Publications, 2025. https://doi.org/10.1201/9781003533023
[18] Irvin, Allison; Kiral, Isabell. Designing for privacy and confidentiality on distributed ledgers for enterprise (industry track). In Proceedings of the 20th International Middleware Conference Industrial Track, pp. 22-28, 2019.
[19] Muralidhara, Pavan. IoT Applications in Cloud Computing for Smart Devices. International Journal of Computer Science and Technology, 1(1), 1-41, 2017. https://ijcst.com.pk/index.php/IJCST/article/view/239
[20] Khan, Latif U.; Yaqoob, Ibrar; Tran, Nguyen H.; Kazmi, SM Ahsan; Dang, Tri Nguyen; Hong, Choong Seon. Edge-computing-enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal, 7(10), 10200-10232, 2020.
[21] Labonnote, Nathalie. AI-driven sustainable cities: A Nordic-inspired requirement framework. SHS Web of Conferences, 198, 03001. EDP Sciences, 2024.
[22] Bojkovic, Z.S.; Milovanovic, D.A.; Fowdur, T.P. (Eds.). 5G Multimedia Communication: Technology, Multiservices, and Deployment (1st ed.). CRC Press, 2020. https://doi.org/10.1201/9781003096450
Journal Information
| Frequency: Quarterly |
| Submission to First Decision: 7 days |
| Submission to Acceptance: 30 days |
| Accept to Publish: 15 days |
| Article Processing Charges (APCs): None |
