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Open access 2026

Cooperative Task Offloading in Mobile Edge Computing via an Improved MASAC Framework

An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.

Zheng Yao, Jie Liu, Changjun Deng et al. · 0 citations
Open access Jul 2026

Multi-Objective Balanced Optimization Task Offloading Algorithm Based on Multi-Agent Collaboration

A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.

Hui Li, Zhilong Zhu, Wanwei Huang et al. · 0 citations
Jul 2026

Stable matching based efficient task offloading in heterogeneous edge environment

A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.

Shoulu Hou, Zhenquan Liu, Yaru Zhao et al. · 0 citations
Jul 2026

Intelligent Cooperative Computation Offloading and Resource Allocation for Dual-Dependency Tasks in Edge Computing

Mobile edge computing (MEC) has accelerated the development of artificial intelligence and Internet of Things technologies, leading to the explosive growth of intelligent applications characterized by resource intensity and latency sensitivity, such as image processing and smart home. In practice, an application typically consists of multiple tasks with execution dependencies, where the output of some tasks serves as the input for specific others. Recently, the design of computation offloading methods for such execution-dependent tasks has received extensive research. However, computation offloading for execution-dependent tasks with service dependencies in resource-constrained multi-user, multi-edge-server cooperative MEC systems has not been thoroughly studied. In this paper, we formulate a cooperative computation offloading problem for dual-dependency tasks in multi-edge-server scenarios with limited service and computing resources, aiming to minimize the long-term average service delay for multiple users. To solve this problem, we propose a recurrent multi-agent reinforcement learning-based dual-dependency task offloading (RMA-DepO) algorithm, which enables users to communicate during training to explore and learn optimal joint task offloading and computing resource allocation strategies, and to make distributed offloading decisions at execution time. Simulation results demonstrate that the proposed RMA-DepO algorithm outperforms several baselines under different network settings, demonstrating its effectiveness in coordinating edge resources for cooperative computation of dual-dependency tasks.

Zhixiu Yao, Yun Li, Qilie Liu et al. · 0 citations
Open access Aug 2026

Improved PSO-Based Task Offloading Model for Internet of Vehicles Edge Computing

: The demand for computer resources for internet of vehicles services like autonomous driving, real-time navigation, and in-vehicle entertainment has grown rapidly due to the widespread deployment of intelligent transportation systems and the ongoing advancement of information and communication technologies. Therefore, a novel task offloading optimization allocation model for internet of vehicles edge computing is proposed. The model is based on mobile edge computing architecture. Through clustering algorithm, it intelligently clusters all nodes in the static parked vehicles edge computing architecture. Moreover, the PSO algorithm is coded and optimized, which improves the efficiency and resource utilization of internet of vehicles task offloading. The experimental results indicated that the model was able to realize obvious inter-cluster separation under 2 min, 10 min, 50 min, and 100 min time nodes. The vehicles inside the clusters were also more closely distributed, resulting in good internal consistency and external separation. When the number of tasks was increased to 60, the corresponding total system cost of the research model was only 198. When the task computation volume was 120 GHZ, the total system cost of the research model was only 214. In addition, the research model still maintained a high offloading success rate of 97.5%, 94.6%, and 92.8 in low-density, medium-density, and high-density environments. In summary, the research model not only can effectively improve the vehicle task processing efficiency and reduce the system overhead, but also shows strong adaptability and robustness, which has good prospects for practical applications.

Zhixiong Jin · 0 citations
Open access Aug 2026

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.

Tanmay Baidya, S. Moh · 0 citations

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