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<p>Mobile edge computing has gained significant attention in smart grid IoT, as it is seen as a promising technique for supporting computation-heavy services. Efficient online task processing is crucial in this context, as it ensures real-time decision-making and system responsiveness, which are vital for maintaining grid stability and optimizing resource management. However, the challenge of meeting online service requirements within the constraints of limited resources and strict task processing delay persists. To address this, we propose an online delay-aware online mobile computation offloading scheme consisting of four crucial algorithms, which firstly classify users into heterogeneous networks and then design the online resource allocation methods on the macro base station (MBS) and small base stations (SBSs), respectively, and finally design the updating strategy of the control parameters to ensure the load balancing among bases. Simulation results demonstrate that for the case of 50 mobile users, the proposed algorithm reduces task execution delay by 42.2%, 44.4%, and 62.9% relative to the three baseline algorithms, which allow the tasks to be executed only at the MBS, SBS or to be executed locally.</p>

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Lightweight and delay-aware resource management scheme in smart grid IoT networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月11日

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  • Danni Liu,
  • Shengda Wang,
  • Xiaofu Sun,
  • Chunyan An,
  • Weijia Su,
  • Jiakang Liu

摘要

Mobile edge computing has gained significant attention in smart grid IoT, as it is seen as a promising technique for supporting computation-heavy services. Efficient online task processing is crucial in this context, as it ensures real-time decision-making and system responsiveness, which are vital for maintaining grid stability and optimizing resource management. However, the challenge of meeting online service requirements within the constraints of limited resources and strict task processing delay persists. To address this, we propose an online delay-aware online mobile computation offloading scheme consisting of four crucial algorithms, which firstly classify users into heterogeneous networks and then design the online resource allocation methods on the macro base station (MBS) and small base stations (SBSs), respectively, and finally design the updating strategy of the control parameters to ensure the load balancing among bases. Simulation results demonstrate that for the case of 50 mobile users, the proposed algorithm reduces task execution delay by 42.2%, 44.4%, and 62.9% relative to the three baseline algorithms, which allow the tasks to be executed only at the MBS, SBS or to be executed locally.