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

(2025) 2025:14 Liu et al. J Wireless Com Network https://doi.org/10.1186/s13638-025-02434-3 EURASIP Journal on Wireless Communications and Networking Open Access RESEARCH Lightweight and delay‑aware resource management scheme in smart grid IoT networks Danni Liu1†, Shengda Wang1†, Xiaofu Sun1†, Chunyan An2†, Weijia Su1*† and Jiakang Liu2† † Danni Liu, Shengda Wang, Xiaofu Sun, Chunyan An, Weijia Su, and Jiakang Liu have contributed equally to this work. *Correspondence: 1 JiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation Ltd, Changchun 130000, Jilin, China 2 State Grid Smart Grid Research Institute Co., Ltd., Electric Power Intelligent Sensing Technology and Application of State Grid Corporation Joint Laboratory, Beijing 102209, China Abstract 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. Keywords: Smart grid IoT networks, Control parameter, Competitive ratio 1 Introduction In recent years, the explosive growth of real-time applications on IoT users has led to a surge in resource demands and strict processing requirements. Mobile edge computing (MEC), which enables the proximity of IoT users, is replacing traditional cloud computing as the key technology to support delay-sensitive applications [1–3]. Thus, the problem of minimizing delay cost in a real-time scenario is becoming more and more recurrent [4–6]. Mobile edge computing offers cloud services at the network periphery to reduce service latency and backhaul load and enhance the quality of experience or security of IoT users [7, 8]. By improving computation capabilities at the network edge, MEC can effectively reduce the computation delay of IoT users. However, in many traditional MEC deployments, MEC services are installed only at macro base stations (MBSs) or small base stations (SBSs), creating a mismatch between user locations and realtime application demands [7, 9, 10]. Therefore, a two-tier small-cell network architecture with SBSs and MBS taking advantage of MEC is needed, as it achieves higher © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Liu et al. J Wireless Com Network (2025) 2025:14 computation capability than conventional small-cell networks and improves resource utilization [11–13]. At the same time, a major problem appears in small-cell networks, where heavy data traffic from computation-intensive applications causes network congestion [14, 15]. Hence, the backhaul link between the small base stations and the macro base station is considered to ease the computation pressure on SBSs. Based on this need, we propose a backhaul transmission policy that allows tasks to be offloaded from SBSs to MBSs, thereby relieving network congestion. However, load balancing in two-tier networks also challenges the processing time of real-time applications due to limited CPU resources on MEC servers and bandwidth constraints in base stations. If the transmission rate on certain links becomes too high during peak hours, servers may overload, forcing some tasks to wait in queue or even be dropped. Conversely, a poor transmission rate may cause underutilization of MEC servers in slack periods. Thus, a load balancing problem between SBSs and MBS is necessary [16]. To address this challenge, we design a network architecture that integrates control parameters to adjust the transmission rates of SBSs and MBS in real time, thereby balancing the workload more effectively. In this paper, we present a delay-aware mobile edge computing scheme (DAMCO) in a real-time scenario with a collaborative system of macrocells and SBSs. Because load imbalances between MBS and SBSs can increase offloading delay and waste computing resources, we introduce two control parameters that update in real time according to the resource usage of MBSs and SBSs. We also develop a delay-aware online mobile computation offloading scheme comprising four subalgorithms, which initiates with the classification of users across heterogeneous networks and then formulates online resource allocation approaches for both the MBS and SBSs; finally, an updating strategy for the control parameters is devised to guarantee the load balancing among the base stations. To evaluate the efficiency and effectiveness of DAMCO, we introduce an offline algorithm based on the particle swarm optimization (PSO) approach as our benchmark, since it can take advantage of complete information about the network conditions and make optimal resource allocation decisions based on the global knowledge. We also analyze the competitive ratio of our solution and validate its performance through simulations. Compared with other benchmarks, our results show that DAMCO reduces network delay and maintains load balancing effectively. The main contributions of this paper are summarized as follows: • We investig (...truncated)