Energy-Aware Task Offloading in 6G-Enabled Mobile Edge Computing Environments

Year : 2026 | Volume : 04 | Issue : 01 | Page : 14 19
    By

    Jyoti AmitKumar Dhamecha,

  1. Assistant Professor, Department of Computer Science and Engineering, Sardar Patel College of Administration and Management, Gujarat, India

Abstract

The emergence of sixth-generation (6G) wireless communication networks is expected to revolutionize future mobile systems by enabling ultra-low latency communication, extremely high data rates, massive connectivity, and intelligent network management. In parallel, mobile edge computing (MEC) has gained significant attention as a promising paradigm that brings computational resources closer to end users, thereby alleviating network congestion and reducing end-to-end service delays. Despite these advantages, the rapid growth of computation-intensive and latency-sensitive applications, such as extended reality, autonomous systems, and intelligent healthcare, poses serious challenges in terms of energy consumption, particularly for battery-powered mobile devices. As a result, energy efficiency has become a critical performance metric in 6G-enabled MEC environments. To address this issue, energy-aware task offloading has emerged as a key research area, aiming to determine when, where, and how computational tasks should be executed to minimize energy usage while meeting strict latency requirements. This paper provides a comprehensive and descriptive analysis of existing energy-aware task offloading strategies in 6G-MEC systems. It systematically reviews optimization approaches, including heuristic methods, mathematical optimization, and emerging artificial intelligence–driven and edge intelligence techniques. Furthermore, the paper analyzes the inherent trade-offs between energy consumption, computational delay, and system scalability. Finally, it highlights open challenges, such as dynamic network conditions, heterogeneous resources, and sustainability concerns, and outlines research directions toward achieving intelligent, adaptive, and energy-efficient 6G-enabled MEC architectures.

Keywords: 6G wireless networks, artificial intelligence optimization, computation offloading, edge intelligence, energy efficiency, energy-aware task offloading, latency–energy trade-off, mobile edge computing (MEC), sustainable networking, ultra-low latency communication

[This article belongs to International Journal of Mobile Computing Technology ]

How to cite this article:
Jyoti AmitKumar Dhamecha. Energy-Aware Task Offloading in 6G-Enabled Mobile Edge Computing Environments. International Journal of Mobile Computing Technology. 2026; 04(01):14-19.
How to cite this URL:
Jyoti AmitKumar Dhamecha. Energy-Aware Task Offloading in 6G-Enabled Mobile Edge Computing Environments. International Journal of Mobile Computing Technology. 2026; 04(01):14-19. Available from: https://journals.stmjournals.com/ijmct/article=2026/view=248041


References

  1. Hamdi H. Can e-payment systems revolutionize finance of the less developed countries? The case of mobile payment technology. Int J Econ Financ Issues. 2011;1(2):46–53.
  2. Saad W, Bennis M, Chen M. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Netw. 2020;34(3):134–142. doi:10.1109/MNET.001.1900287.
  3. Mao Y, Zhang J, Song SH, Letaief KB. Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans Wirel Commun. 2017;16(9):5994–6009. doi:10.1109/TWC.2017.2717986.
  4. Wang F, Xu J, Wang X, Cui S. Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans Wirel Commun. 2018;17(3):1784–1797. doi:10.1109/TWC.2017.2785305.
  5. Zhang N, Guo S, Dong Y, Liu D. Joint task offloading and data caching in mobile edge computing networks. Comput Netw. 2020;182:107446. doi:10.1016/j.comnet.2020.107446.
  6. Dai Y, Zhang K, Maharjan S, Zhang Y. Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond. IEEE Trans Veh Technol. 2020;69(10):12175–12186. doi:10.1109/TVT.2020.3013990.
  7. You C, Huang K, Chae H, Kim BH. Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans Wirel Commun. 2017;16(3):1397–1411. doi:10.1109/TWC.2016.2633522.
  8. Zhou H, Jiang K, Liu X, Li X, Leung VCM. Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing. IEEE Internet Things J. 2022;9(2):1517–1530. doi:10.1109/JIOT.2021.3091142.
  9. Letaief KB, Shi Y, Lu J, Lu J. Edge artificial intelligence for 6G: Vision, enabling technologies, and applications. IEEE J Sel Areas Commun. 2022;40(1):5–36. doi:10.1109/JSAC.2021.3126076.
  10. Zhu G, Lyu Z, Jiao X, Liu P, Chen M, Xu J, Cui S, Zhang P. Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G. Sci China Inf Sci. 2023;66(3):130301. doi:10.1007/s11432-022-3652-2.
Regular Issue Subscription Review Article
Volume 04
Issue 01
Received 30/01/2026
Accepted 03/02/2026
Published 20/03/2026
Publication Time 49 Days
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