Aug 2026· Tehnički Vjesnik· Vol 33· 0 citations· 32 references
TL;DR
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.
Abstract
: 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.
With the rapid development of intelligent vehicles and the Internet of Things (IoT), Internet of Vehicles (IoV) technology is gradually being applied in more streets. However, traditional cloud computing is unable to meet the higher requirements of the IoV system. Mobile edge computing (MEC), which is suitable for mobile networks, provides a better paradigm for intelligent transportation systems (ITS). This paper proposes a joint task offloading method based on MEC (JTOM) in the IoV environment. Firstly, it obtains the sequence of transferable routes for onboard tasks in the system. Then, it determines the offloading location of the tasks based on the task requirements and computing resources. Finally, it optimizes multiple objectives using the non-dominated sorting genetic algorithm II (NSGA-II) with the latency and energy consumption as the optimization goals. In the end, experiments are conducted to prove the feasibility of JTOM.
Yi-Long Sun, Lu Liang, Ya-Mei Cai et al.· International Conference on...· 0 citations
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
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.
A framework based on GTGO to jointly offload, schedule and allocate resources to different tasks and augment it with an integrated explainable AI (XAI) module is presented, indicating that the suggested framework is an effective, efficient, and transparent resource management solution in intelligent vehicular edge computing systems.
Aditi Moudgil, S. Rani, Fazlullah Khan· PLoS ONE· 0 citations
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.· Computers, Materials & C...· 0 citations