Jul 2026· International Conference on Computer Communications and Networks· pp. 1-6· 0 citations· 12 references
Abstract
This paper presents a novel approach for task offloading in Software Defined Networking (SDN)-based vehicular networks based on a multi-objective optimization algorithm which uses Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Long Short-Term Memory (LSTM)-based vehicle trajectory prediction. The proposed solution addresses key challenges such as energy consumption, communication and computation delays, load balancing, task deadlines, and task division into sub-tasks. By leveraging SDN’s centralized control plane and multi-controller architecture, the framework efficiently manages resources in dynamic vehicular environments. Extensive simulations using real-world vehicular mobility datasets demonstrate that our SDN-enabled task offloading framework for NSGA-II based vehicular task offloading significantly improves task completion time, energy consumption, computation delay,load balancing, and overall resource management compared to existing solutions.
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
The rapid expansion of Internet of Things (IoT) networks necessitates efficient task scheduling and offloading mechanisms to improve energy efficiency, reduce latency, and optimize resource utilization. However, conventional scheduling approaches often suffer from imbalanced workload distribution, high energy consumption, and increased processing delays, resulting in suboptimal system performance. To overcome these issues, this study proposes a Double Fuzzy Clustering-Driven Context Neural Network (DFC-CNN) integrated with the Secretary Bird Optimization Algorithm (SBOA) for energy-aware task scheduling and offloading in Software-Defined Networking (SDN)-enabled IoT environments. The DFC-CNN model dynamically clusters IoT tasks based on contextual attributes, enabling adaptive scheduling, efficient load balancing, and real-time task prioritization. Simultaneously, SBOA optimizes task scheduling and offloading decisions, improving fog and cloud resource utilization while reducing energy consumption and execution delay. Extensive experimental evaluations conducted on benchmark datasets demonstrate that the proposed framework achieves up to 35% lower energy consumption, 28% shorter task completion time, and 40% higher system throughput compared with state-of-the-art methods, including PSO, FA, SSA, HHO, MOMFO, and ABC. By integrating context-aware task clustering with nature-inspired optimization, the proposed framework enhances scalability, improves resource utilization, and supports sustainable and energy-efficient computing in large-scale IoT environments.
Unknown authors· International Journal of Com...· 0 citations
6G vehicular services, including cooperative perception, augmented reality navigation, and high-definition map updating, need computation support close to moving vehicles. Vehicular Edge Computing (VEC) is a natural solution, but the offloading decision becomes difficult when wireless channel conditions, vehicle density, and edge server loads vary simultaneously. In this paper, we study joint task offloading and resource allocation in 6G VEC with high- and low-frequency cooperation (HL-FC). We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP). Each vehicle decides its offloading ratio, transmission power, server association, and edge CPU request from local observations. To evaluate the proposed policy, we build a lightweight equation-driven Python simulator and compare MAPPO with Local-only, Edge-only, Random, and Greedy policies. Compared with Edge-only, MAPPO reduces the average system cost by 32.15%, 23.51%, and 17.13% under 10, 15, and 20 vehicles, respectively. It also improves the task completion rate by 21.00, 20.49, and 17.65 percentage points. Additional blockage experiments show that HL-FC keeps the policy more robust than high-frequency-only transmission under severe high-frequency blockage. The results reveal that MAPPO delivers better performance when edge resources become congested than in lightly loaded scenarios.
Zi-Heng Gu· 2026 8th International Confe...· 0 citations
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
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.
