Recently, the combination of Internet of Vehicles (IoV) and blockchain has emerged as a promising solution for enhancing the security and efficiency in vehicular communication networks. However, the deployment of blockchain technique in IoV inevitably derives additional computation and communication overheads, which significantly hinders the development of IoV. In addition, efficient task offloading in IoV is essential to support computation‐intensive and delay‐sensitive vehicular services under dynamic network conditions. To address the above challenge, this paper proposes a deep reinforcement learning‐based joint task‐offloading framework for blockchain‐empowered IoV communication networks. Specifically, it formulates the blockchain‐based task‐offloading problem in IoV as a continuous control Markov decision process, aiming at improving long‐term system performances by jointly optimizing latency, computational cost, throughput and security. Then, a twin delayed deep deterministic policy gradient‐based algorithm is customized to learn the optimal offloading policy efficiently in high‐dimensional continuous action space. Furthermore, a trust‐aware mechanism is incorporated into the state representation and reward design to mitigate the impact of malicious vehicles. Finally, simulation results demonstrate that the proposed method outperforms conventional baseline methods with respect to communication latency, computational cost, throughput and security.
The proposed OIBTO framework employs a lightweight Proof-of-Authority consensus within a two-tier architecture consisting of a vehicle layer and an edge layer, and proposes an Improved Starfish Optimization Algorithm (ISFOA) that utilizes chaotic mapping and genetic mutation to optimize offloading decisions and task partitioning ratios, aiming to minimize a priority-weighted combination of latency and energy consumption.
This paper proposes a secure and collaborative computation offloading framework with dynamic service caching in blockchain-empowered UAV-assisted vehicular networks, which consists of offloading decision making, dynamic service caching and caching updating decisions, computation resource allocation, and blockchain consensus mechanism.
A blockchain-based dynamically adaptive restructuring framework that enables real-time IoV cluster restructuring by splitting overloaded IoVs to reduce communication overhead, or merging nearby IoVs to optimize resource utilization, offering a proactive, adaptive security paradigm for intelligent transportation frameworks.
Jiawei Shi, Yebo Feng, Konglin Zhu et al.· ACM Transactions on Internet...· 0 citations
Mobile Cloud Computing (MCC) enhances resource-constrained mobile devices by enabling task offloading to edge and cloud environments, but faces challenges in security, scalability, and dynamic resource management. This study proposes a secure, blockchain-enabled federated deep learning framework integrated with Hierarchical Adaptive Scalable Spiking Reinforcement Learning (HASSRL) for intelligent and privacy-preserving task offloading. A hybrid optimization approach combining Revolution Optimization Algorithm (ROA) and Proximal Policy Optimization (PPO) is introduced to achieve adaptive scheduling, efficient resource allocation, and real-time decision-making. The framework incorporates continuous monitoring and a feedback-driven self-optimization mechanism to improve latency, energy consumption, and overall system performance. Experimental evaluation using standard datasets demonstrates that the proposed model achieves high accuracy and efficiency, making it suitable for scalable and real-time MCC applications in next-generation networks. The proposed Blockchain-enabled Federated Deep Learning with HASSRL and ROA–PPO achieved a latency of 48.689 ms, energy consumption of 63.222 mJ, and CPU utilization of 95.705%, while maintaining a throughput of 42.067 Mbps and task completion time of 2.781 s. Through experimental evaluation on the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the framework demonstrated efficient task execution, improved resource utilization, and enhanced adaptability in dynamic MCC environments, ensuring secure, scalable, and real-time intelligent task offloading
Cheruku Poorna, Venkata Srinivasa, Rao Research et al.· International Conference Com...· 0 citations
An innovative decentralized framework that integrates blockchain-based tokenization with a Knowledge-Underpinned Layer (KUL) to enable incentive-based intelligent routing and optimizes routing decisions by integrating semantic insights and contextual data, allowing for more informed, context-aware path selection.
Nureddin A. F. Aldali· International Science and Te...· 0 citations
This work introduced an integrated cross‐layer framework featuring three innovative algorithms: mobility‐aware black hole clustering (M‐BHC), energy‐aware piranha optimization algorithm (EPOA), and cross‐layer multi‐attribute blockchain routing with congestion control (CL‐MABRC).
K. Satheshkumar, S. Ramalingam, A. Suresh Babu et al.· International Journal of Com...· 0 citations