Jul 2026· International Conference on Edge Computing [Services Society]· pp. 122-127· 0 citations· 11 references
Abstract
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.
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
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.
The advancement of vehicle technology enables it to handle complex tasks such as augmented reality, automatic parking, and driving, which demand significant computational power and are sensitive to delays. However, the computational capabilities of these vehicles are limited. As a result, Vehicle Edge Computing (VEC) has been introduced to offload some computational burdens to neighbouring vehicles and edge servers. The main challenge of this system lies in managing offloading in scenarios with frequent disconnections and high-speed driving. To address this challenge, we have formulated the task offloading problem with the goal of minimizing overall time, considering its NP-hard nature and multiple constraints. Our proposed solution is a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading. In the food foraging process, scouts are sent to locate nearby area, which in our case are edge servers and gather contextual details, and upon their return, they perform a dance resembling the infinity symbol, where the central angle indicates the direction of the food field and the speed of the dance signifies the quality of the found food source. The vehicle client uses this information to select one edge server to be in charge of the offloading process. Subsequently, we apply the genetic algorithm to generate an optimized allocation scheme of tasks by considering contextual details of nearby nodes. These details include computational capacity, availability, location, and driving speed. The optimization operators of the genetic algorithm are used to determine the assignment of tasks to nodes. Extensive simulations have demonstrated that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (PSO), and the multi-decision based offloading (MDO).
Mohamed Kamel Benbraika, O. Kraa· Electrotehnică, electronică,...· 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.
Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading scheme (OFA-offloading). Although the exact offloading failure probability is difficult to obtain, it is determined by the service capability of the selected service vehicle (SV). Thus a new tractable metric, i.e., vehicle service capability (VSC), is defined to reflect the offloading failure probability, which is a function of vehicle mobility, resource availability, and link status. Based on VSC of each SV and considering that delay is important for VFC networks, an OFA delay utility is designed. Aiming to maximize this utility, a joint offloading SVs selection and computing resource allocation optimization problem is formulated. Since it is NP-hard and the VFC network is highly dynamic, a novel Graph Neural Network based federated Advantage Actor-Critic (GNN-FAC) algorithm is proposed to solve the problem. GNN-FAC can proactively predict environmental dynamics and incorporate VSC as a critical criterion for offloading decisions. Simulation results demonstrate that compared with existing offloading algorithms, OFA-offloading can improve the OFA delay utility by up to 40%.
Yihao Wu, Yanli Qi, Yiqing Zhou et al.· IEEE Transactions on Network...· 0 citations
To address the urgent practical challenge of service migration for connected autonomous vehicles (CAVs) in mobile edge computing (MEC), this study aims to maximize the task completion rate, particularly for safety-critical operations. Existing approaches often overlook the completion status of tasks with different priority levels during frequent service migrations and fail to co-optimize multiple constraints such as energy consumption, latency, and offloading cost. Consequently, it remains difficult to reliably complete highly urgent tasks in resource-constrained edge environments. To tackle this issue, we propose a comprehensive two-stage solution: the Improved Task Offloading and Service Migration (ITOSM) algorithm. In the first stage, a weighted evaluation model based on information entropy is constructed by integrating transmission time, execution time, and offloading cost. Tasks are offloaded to the edge server with either the highest or second-highest weighted sum according to their urgency level. In the second stage, service migration decisions are optimized using an improved binary particle swarm optimization (BPSO) algorithm with enhanced local search capability. Experimental results demonstrate that ITOSM outperforms existing methods, achieving up to 10.00% higher completion rates for Extremely Important Tasks (EITs) with strict deadlines. This improvement directly contributes to safer and more reliable CAV operations, highlighting the practical significance of this work for intelligent transportation systems.