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.
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.
Xiaofeng Gong, Lang Li, Jiaxing Li et al.· Transactions on Emerging Tel...· 0 citations
A three-layer collaborative framework comprising cloud, edge, and blockchain layers enhances both scheduling efficiency and data reliability, supporting the digital and intelligent transformation of shipbuilding enterprises.
Ganlong Wang, Jun Zhu, Guoyin Zhang et al.· ICST Transactions on Scalabl...· 0 citations
A two-layer blockchain-based VEC (TBVEC) task offloading framework, which extends consensus nodes to parked vehicles (PVs) to improve the scalability of edge computing and demonstrates the security of task offloading and overall system efficiency.
Guoling Liang, Feng Zhao, Chunhai Li et al.· Mathematics· 0 citations
A new hybrid bioinspired optimization framework for efficient blockchain mining is presented, integrating Genetic Algorithm, Firefly optimization, and Particle Swarm Optimization into a unified architecture to take advantage of their complementary strengths.
K. Jajulwar, Priya Dasarwar, Uma Shankar Yadav et al.· Engineering, Technology &...· 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
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