Aug 2026· 2026 International Conference on Intelligent Multimedia, Networking, and Security (IMNS)· pp. 1-6· 0 citations· 18 references
Abstract
With the advancement of autonomous driving and smart navigation, Internet of Vehicles (IoV) systems face stringent requirements for real-time data delivery and processing reliability. Traditional metrics cannot fully capture information timeliness due to network dynamics and packet loss. Existing approaches also struggle with the coupling between task offloading and resource allocation, lacking adaptability in dynamic IoV environments. To address these issues, we propose a joint optimization scheme using a deep Q-network (DQN). Specifically, we build an IoV system model incorporating V2V and V2I communication, and formulate an optimization problem to minimize the average age of information (AAoI) under delay, bandwidth, computing, and energy constraints. We then design a mixed-action DQN algorithm with dual-network architecture, experience replay, and an action mask mechanism to enhance training stability and environmental adaptability. Simulation results show that our DQN-based scheme achieves the lowest AAoI among Random, Greedy, A2C, and DDQN, with reductions of 29.5%, 8.9 %, 7.1 %, and $\mathbf{7. 6 \%}$, respectively. It also exhibits superior delay and energy performance, confirming its effectiveness for dynamic IoV task offloading and resource allocation.
: Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge, UAVs, and satellites; (2) a Reliability-Aware Multi-Objective Optimization Framework (RA-MOOF) that introduces explicit reliability guarantees through cross-layer link reliability modeling, node availability estimation, and smooth reliability proxy functions. Addressing the heterogeneous communication characteristics of the SAGIN architecture, this paper establishes a complete cross-layer delay model and composite reliability metrics. The reliability formulation is defined under explicitly stated conditional-independence assumptions, and the proposed smooth constraint terms are treated as surrogate CMDP costs rather than exact hard chance-constraint guarantees. Extensive experiments in a SAGIN simulation environment demonstrate that the proposed method improves the task completion rate by 3.8%, reduces average latency by 11.1%, and increases system reliability by 3.9% compared to state-of-the-art benchmarks. The optimization-only RA-Opt baseline is used as a non-real-time optimization reference for assessing reliability-aware offloading decision quality, while deployment-time decision-latency comparisons are interpreted primarily among learned inference policies. Comprehensive ablation studies and statistical validation across multiple random seeds confirm the contributions of each component, while cross-layer offloading decision analysis verifies the effectiveness of the method across different network layer selections.
Fei-Yan Bu, Zheng Wang, Yong Pan et al.· Computers, Materials & C...· 0 citations
As the Multimedia Internet of Things (M-IoT) evolves, the orchestration of numerous resources that offer support for high-bandwidth, low-latency applications arises as a key challenge. Architecturally, the edge-cloud framework alleviates structural concerns, but the linked nature of compute and data transfer poses problems of resource management. Approaches that tackle task offloading and adaptive transmission that think independently of each other tend to have problems such as user-server cross-region overloads or network congestion. This paper presents JATO, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning. JATO offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality. The framework was implemented for evaluation with a combination of different edge devices in a testbed alongside a simulation environment. JATO recorded a result of 0.9321 as the holistic score of the overall framework endpoint, a score significantly better than that of all the other frameworks that were used as functional baselines. JATO was able to resource optimally with a network lag of 131.65 milliseconds and a network freeze of 0.09% with the resources utilized. This is evidence that offloading and rate control in combination provides better resource elasticity for M-IoT systems.
G. Purnama, Irma Amelia Dewi, A. Langi et al.· Journal of ICT Research and...· 0 citations
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
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
Integrated sensing, communication, and computation (ISCC) provides a critical enabling platform in supporting the diverse services in the Internet of Vehicles (IoV). However, effective heterogeneous IoV service provisioning relies on both communication-centric and beyond-communication performance metrics, making unified resource allocation challenging. Moreover, competition from concurrent services for limited multi-dimensional resources is intensified in dynamic vehicular environments. In this paper, we investigate the resource allocation problem for concurrent communication and target classification services in an ISCC-enabled IoV system. To solve the problem, we first introduce the value of service (VoS) to unify communication rate and classification accuracy into a common measure that captures the degree of heterogeneous service fulfillment. To reduce the complexity of dynamic problem optimization, we propose a digital twin-assisted proximal policy optimization (DTPPO) algorithm, in which the digital twin exploits both current and historical information to generate predictive information, thereby enhancing policy learning in dynamic environments. Furthermore, we develop a large language model (LLM)-enhanced DTPPO (LLM-DTPPO) algorithm, which leverages the contextual understanding and domain knowledge of LLMs to reshape the reward function and improve resource allocation performance under multi-dimensional resource competition. Simulation results based on real-world vehicle mobility traces demonstrate that the proposed algorithms outperform existing benchmark schemes.
Bangzhen Huang, Zhang Liu, Lianfen Huang et al.· IEEE Transactions on Network...· 0 citations
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
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