Research on UAV-assisted Dynamic-weight Edge Computing Offloading Strategy

WANG Yijun; WANG Yachu; SHAHD Batool; MIAO Ruixin

doi:10.11999/JEIT260054

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WANG Yijun, WANG Yachu, SHAHD Batool, MIAO Ruixin. Research on UAV-assisted Dynamic-weight Edge Computing Offloading Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260054

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WANG Yijun, WANG Yachu, SHAHD Batool, MIAO Ruixin. Research on UAV-assisted Dynamic-weight Edge Computing Offloading Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260054

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Research on UAV-assisted Dynamic-weight Edge Computing Offloading Strategy

doi: 10.11999/JEIT260054 cstr: 32379.14.JEIT260054

School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China

Funds: The National Natural Science Foundation of China (61771219), Jilin Provincial Natural Science Foundation (20250102227JC)

Received Date: 2026-01-15
Accepted Date: 2026-03-24
Rev Recd Date: 2026-03-17

Available Online: 2026-04-22

Abstract

Abstract

Objective The increasing demands of the Internet of Things (IoT) for computational resources and real-time processing have highlighted the significance of Mobile Edge Computing (MEC). Traditional MEC relies on terrestrial base stations, resulting in coverage blind spots in remote or specialized environments. Unmanned Aerial Vehicle (UAV)-assisted MEC architectures exploit UAVs’ flexible deployment to expand service coverage. However, existing approaches for multi-terminal, multi-UAV scenarios often fail to optimize task offloading latency, system energy consumption, and adaptability to dynamic environments simultaneously. They also overlook optimal UAV selection when terminal devices are covered by multiple UAVs and lack adaptive mechanisms to adjust optimization objectives during task execution. This study addresses these challenges by integrating cooperative caching, offloading decision-making, and resource allocation strategies. Methods A three-tier microcloud-edge-terminal architecture is constructed, comprising a central cloud, multiple UAV edge servers with caching capabilities, and numerous mobile terminal devices. A cooperative caching mechanism reduces transmission delay during task execution. Task offloading adopts a fine-grained partial offloading mode, dividing complex tasks into dependent subtasks modeled through a Directed Acyclic Graph (DAG). The Cooperative Caching-Adaptive Hierarchical MultiVerse Optimizer (CCAH-MVO) algorithm is proposed. A hybrid coding scheme encodes offloading decisions, caching decisions, and resource allocation uniformly. A dynamic weight mechanism adaptively balances delay and energy consumption according to the system’s real-time energy state. Additionally, a UAV selection strategy is implemented for scenarios where terminals are covered by multiple UAVs. By simulating inter-universe material exchange and local refined search, the algorithm efficiently determines the optimal offloading strategy. MATLAB simulations validate the method under various experimental settings. Results and Discussions The simulation scenario involves 50 randomly distributed terminal devices and 5 UAVs in a 400 m × 400 m area. UAVs are deployed above terminal cluster centers, while terminals at cluster edges are simultaneously within the coverage of multiple UAVs (Fig. 5). The optimal UAV for each terminal is selected using the UAV selection function (Fig. 6), preventing resource bottlenecks and achieving balanced load distribution. In terms of delay performance, the CCAH-MVO algorithm maintains the lowest task delay across all task volumes, with a gradual increase as the number of tasks grows (Fig. 7). Delay under CCAH-MVO is consistently lower than that under fixed-weight strategies across the full task range, demonstrating the effectiveness of the dynamic adaptive mechanism in preserving low latency (Fig. 10). For energy consumption, differences among the algorithms are minor when task quantities are low. Under high task loads, the activation of the dynamic weight mechanism flattens the energy consumption curve (Fig. 8). When the number of tasks reaches 100, total energy consumption under CCAH-MVO is the lowest among all strategies and remains lower than the fixed-weight approach, reflecting effective control under critical energy conditions (Fig. 9). Regarding total system overhead, the CCAH-MVO algorithm consistently achieves the best performance. The gap with fixed-weight strategies widens when task numbers exceed 80, illustrating the dynamic weight mechanism’s collaborative optimization of delay and energy consumption (Fig. 11). Overall, by integrating the dynamic weight mechanism and balancing load through UAV selection, the CCAH-MVO algorithm effectively mitigates resource constraints and high task processing overhead in complex, dynamic UAV-assisted MEC environments. It ensures precise coordination between task delay and energy consumption across different load stages. Conclusions The proposed CCAH-MVO framework, incorporating a microcloud-edge-terminal architecture, cooperative caching mechanism, fine-grained partial offloading, dynamic weight adjustment, and UAV selection strategy, effectively addresses resource scheduling in complex multi-UAV MEC environments. Simulations show adaptive optimization of objectives, intelligent energy management, low latency, and reduced total system overhead, improving service stability and user experience. This research provides a practical solution for efficient UAV edge computing in dynamic environments. Future work will explore dynamic energy efficiency optimization and multi-node collaboration while maintaining low-latency performance.

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