Energy Efficiency and Awareness in Edge Computing: A Critical Review of Challenges, Strategies, and Future Directions”

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | 03 | Page : –
    By

    Manpreet Singh Gill,

  • Dr. Rajesh Kumar Bawa,

  1. Research Scholar, Department of Computer Science, Punjabi University Patiala, Punjab, India
  2. Professor, Department of Computer Science, Punjabi University Patiala, Punjab, India

Abstract

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Edge computing enhances distributed systems by processing data near its source, yet its rapid growth—driven by IoT, 5G, and smart applications—escalates energy consumption across billions of devices. This paper critically evaluates energy-saving techniques at hardware, software, and network levels, alongside awareness mechanisms like AI-driven tools and user education. It examines trade-offs between efficiency and performance, scalability issues, and standardization gaps. Case studies from smart grids, autonomous vehicles, smart cities, vehicular traffic management, and healthcare highlight practical insights, while future directions explore quantum computing, renewable energy, and policy incentives.

Keywords: Edge Computing, Energy Consumption, IOT, AI, Scalability

How to cite this article:
Manpreet Singh Gill, Dr. Rajesh Kumar Bawa. Energy Efficiency and Awareness in Edge Computing: A Critical Review of Challenges, Strategies, and Future Directions”. Journal of Power Electronics and Power Systems. 2025; 15(03):-.
How to cite this URL:
Manpreet Singh Gill, Dr. Rajesh Kumar Bawa. Energy Efficiency and Awareness in Edge Computing: A Critical Review of Challenges, Strategies, and Future Directions”. Journal of Power Electronics and Power Systems. 2025; 15(03):-. Available from: https://journals.stmjournals.com/jopeps/article=2025/view=0


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References

  1.  Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” *IEEE Communications Surveys & Tutorials*, vol. 19, no. 4, pp. 2322-2358, 2017.
  2. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” *IEEE Internet of Things Journal*, vol. 3, no. 5, pp. 637-646, 2016.
  3. P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” *IEEE Communications Surveys & Tutorials*, vol. 19, no. 3, pp. 1628-1656, 2017.
  4.  S. Deng, L. Huang, J. Taheri, and A. Y. Zomaya, “Computation offloading for service workflow in mobile cloud computing,” *IEEE Transactions on Parallel and Distributed Systems*, vol. 26, no. 12, pp. 3317-3329, 2015.
  5. R. Morabito, V. Cozzolino, A. Y. Ding, N. Beijar, and J. Ott, “Consolidate IoT edge computing with lightweight virtualization,” *IEEE Network*, vol. 32, no. 1, pp. 102-111, 2018.
  6. X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” *IEEE/ACM Transactions on Networking*, vol. 24, no. 5, pp. 2795-2808, 2016.
  7. T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, “On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration,” *IEEE Communications Surveys & Tutorials*, vol. 19, no. 3, pp. 1657-1681, 2017.
  8.  Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: State-of-the-art and research challenges,” *Journal of Internet Services and Applications*, vol. 1, no. 1, pp. 7-18, 2010.
  9.  S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, and A. Jamalipour, “Wireless body area networks: A survey,” *IEEE Communications Surveys & Tutorials*, vol. 16, no. 3, pp. 1658-1686, 2014.
  10.  P. Zanjal, S. Khandelwal, and G. R. Bamnote, “Energy-efficient smart city applications using edge computing,” *IEEE Transactions on Sustainable Computing*, vol. 7, no. 2, pp. 315-326, 2022.
  11.  X. Hou et al., “Vehicular fog computing: A viewpoint of vehicles as the infrastructures,” *IEEE Transactions on Vehicular Technology*, vol. 65, no. 6, pp. 3860-3873, 2016.
  12.  J. Ren et al., “Edge computing for the internet of things,” *IEEE Network*, vol. 32, no. 1, pp. 6-7, 2018.
  13. C. Zhu et al., “Green internet of things for smart world,” *IEEE Access*, vol. 3, pp. 2151-2162, 2015.
  14. X. Lin et al., “An overview of 3GPP device-to-device proximity services,” *IEEE Communications Magazine*, vol. 52, no. 4, pp. 40-48, 2014.
  15. M. Gorlatova et al., “Movers and shakers: Kinetic energy harvesting for the internet of things,” *IEEE Journal on Selected Areas in Communications*, vol. 33, no. 8, pp. 1624-1639, 2015.
  16.  F. Bonomi et al., “Fog computing and its role in the internet of things,” *Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing*, pp. 13-16, 2012.
  17. P. W. Shor, “Quantum computing,” *Documenta Mathematica*, pp. 467-486, 1998.
  18. Y. He et al., “Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach,” *IEEE Transactions on Vehicular Technology*, vol. 67, no. 1, pp. 44-55, 2018.
  19.  N. Zhang et al., “Cooperative content caching in 5G networks with mobile edge computing,” *IEEE Wireless Communications*, vol. 25, no. 3, pp. 80-87, 2018.
  20.  M. Aazam et al., “Deploying fog computing in industrial internet of things and industry 4.0,” *IEEE Transactions on Industrial Informatics*, vol. 14, no. 10, pp. 4674-4682, 2018.
  21. M. Patel et al., “Energy-efficient edge AI chips: Design and deployment for smart cities,” *IEEE Transactions on Sustainable Computing*, vol. 9, no. 1, pp. 112-125, 2024.
  22. S. Kim et al., “Adaptive data compression for 5G-enabled edge networks,” *IEEE Network*, vol. 38, no. 2, pp. 56-64, 2024.
  23. R. Gupta et al., “Federated learning for energy-efficient edge computing,” *IEEE Internet of Things Journal*, vol. 11, no. 4, pp. 2345-2358, 2024.
Ahead of Print Subscription Review Article
Volume 15
03
Received 27/05/2025
Accepted 28/05/2025
Published 30/06/2025
Publication Time 34 Days
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