Research on UAV-Assisted Dynamic Weighted 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 Weighted 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 Weighted Edge Computing Offloading Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260054

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Research on UAV-Assisted Dynamic Weighted 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).

Accepted Date: 2026-03-24
Rev Recd Date: 2026-03-24

Available Online: 2026-04-22

Abstract

Abstract

Objective As the Internet of Things (IoT) continues to evolve with increasing demands for computational resources and real-time processing, the significance of Mobile Edge Computing (MEC) technology has become increasingly prominent. However, traditional MEC heavily relies on terrestrial base stations, resulting in coverage blind spots in remote or specialized environments. Unmanned Aerial Vehicle (UAV)-assisted MEC architectures leverage the flexible deployment capabilities of UAVs to effectively expand service coverage. Nonetheless, in complex scenarios involving multiple user devices and UAV-based servers, existing research remains insufficient in simultaneously optimizing task offloading latency, system energy consumption, and adaptability to dynamic environments. Specifically, current approaches often overlook the optimal server selection problem when terminal devices are covered by multiple UAVs and lack mechanisms for adaptive adjustment of optimization objectives based on system states during task processing. This paper aims to address the challenges of collaborative caching, offloading decision-making, and resource allocation in multi-terminal, multi-UAV scenarios. By introducing a dynamic weighting mechanism and advanced optimization algorithms, the proposed framework seeks to achieve low-latency performance while effectively managing system energy consumption, ultimately reducing total system overhead and enhancing user experience. Methods This paper constructs a "micro-cloud-edge-terminal" three-tier collaborative computing architecture consisting of a central cloud, multiple UAV edge servers with caching capabilities, and numerous mobile terminal devices. In this model, a cache-assisted mechanism is introduced to reduce transmission delay during task execution. Task offloading adopts a fine-grained partial offloading mode, which divides complex tasks into subtasks with dependencies and models them through a directed acyclic graph (DAG). To address the core optimization problem, a Cooperative Caching-Adaptive Hierarchical Multiverse Optimizer (CCAH-MVO) algorithm is proposed. Firstly, a hybrid coding scheme is designed to uniformly encode offloading decisions, caching decisions, and resource allocation. Then, a dynamic weight mechanism is introduced to adaptively adjust the weight ratio of delay and energy consumption in the total overhead according to the system energy consumption state. Additionally, a UAV selection strategy for multi-UAV coverage scenarios is proposed. By simulating inter-universe material exchange and local refined search, the optimal offloading strategy is efficiently found. This method is implemented on the MATLAB platform and validated through extensive simulations under various experimental settings. Results and Discussions To verify the performance of the proposed strategy, 50 randomly distributed terminal devices and 5 UAVs are set up in a 400m×400m simulation scenario. Comparisons are conducted with algorithms such as SCA, PSO-GWO, and I-ACO, as well as strategies including local execution, full offloading, and fixed weights. The initial simulation state shows that the 5 UAVs are deployed above the centers of terminal device clusters, and terminal devices located at the edges of the clusters are simultaneously within the communication coverage of multiple UAVs (Fig. 5). In this scenario, the optimal UAV is selected by calculating the UAV selection function value (Fig. 6), which effectively avoids resource bottlenecks caused by all terminals flocking to a single UAV and achieves balanced load distribution. In terms of delay performance, the CCAH-MVO algorithm maintains the lowest delay across the entire task quantity range, with a gentle increase as the number of tasks grows (Fig. 7). Among different offloading strategies, the delay of the CCAH-MVO algorithm's offloading strategy is lower than that of the fixed-weight strategy throughout the task quantity range, demonstrating the superiority of the dynamic adaptive mechanism in maintaining low delay (Fig. 10). In terms of energy consumption performance, the energy consumption differences among various algorithms are small when the task quantity is low. Under high task load, the energy consumption curve tends to flatten after the dynamic weight mechanism is activated (Fig. 8). When the number of tasks reaches 100, the total energy consumption of the CCAH-MVO strategy is the lowest among all strategies and lower than that of the fixed-weight strategy, reflecting the effective control capability of the dynamic weight mechanism in the critical energy consumption state (Fig. 9). In terms of total system overhead, the CCAH-MVO algorithm consistently maintains the optimal performance, and the gap with the fixed-weight strategy gradually expands when the number of tasks exceeds 80, demonstrating the collaborative optimization capability of dynamic weights for delay and energy consumption (Fig. 11). Overall, by integrating the dynamic weight mechanism and achieving load balancing through the UAV selection strategy, the CCAH-MVO algorithm effectively solves the problems of resource constraints and excessively high total system task processing overhead in complex and dynamic UAV-assisted mobile edge computing environments, realizing precise coordination between delay and energy consumption at different load stages. Conclusions Aiming at the task offloading problem in multi-UAV-assisted mobile edge computing scenarios, this paper proposes a collaborative optimization strategy based on the improved CCAH-MVO algorithm. By constructing a three-tier network architecture, introducing a caching mechanism, adopting fine-grained offloading, and designing dynamic weight and UAV selection strategies, the strategy effectively solves the resource scheduling dilemma in complex environments. Comparative simulation experiments with various strategies confirm that the proposed strategy can adaptively adjust optimization objectives according to the real-time system state, intelligently control energy consumption while ensuring low latency, significantly reduce total system overhead, and improve service stability and user experience. This research provides a valuable solution for the efficient management of UAV edge computing resources in dynamic environments. Future work will further deepen the exploration of dynamic energy efficiency optimization and multi-node collaboration mechanisms while maintaining the advantage of low latency.
- Mobile Edge Computing (MEC),
- Offloading Decision-Making,
- UAV Selection Strategy,
- Dynamic Weight,
- Multi-Verse Optimizer Algorithm (MVO)

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