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
INTRODUCTION: As a critical process in high-end equipment manufacturing, dynamic scheduling in digital workshops plays a vital role in order delivery and project cost control. However, traditional scheduling frameworks suffer from weak data security, opaque multi-agent collaboration, and slow constraint checking.
OBJECTIVES: To address these limitations, this study proposes an efficient and trustworthy dynamic scheduling solution that balances operational efficiency with data reliability.
METHODS: A three-layer collaborative framework comprising cloud, edge, and blockchain layers is designed. The cloud layer employs a multi-strategy improved multi-objective genetic algorithm with three-stage encoding targeting processes, equipment, and job teams. This algorithm integrates tournament selection, adaptive repair, and particle swarm optimization to globally minimize maximum delivery time, total energy consumption, and equipment load fluctuation. The blockchain layer leverages consortium blockchain and smart contracts for tamper-proof data storage and automatic constraint verification, while the edge layer handles real-time on-site scheduling.
RESULTS: The optimization algorithm converges to an optimal value of 55.1 in an average of only 3.9 iterations, outperforming three mainstream heuristic algorithms. In a real deployment across 57 workstations in a cruise ship laser sheet metal workshop, scheduling tasks are completed within 25 seconds. Blockchain technology achieves a 100% data tampering detection rate, improves data consistency from 96.2% to 99.8%, and reduces scheduling deviation from 4.39% to 3.82%, with only a 3-second processing overhead.
CONCLUSION: The integrated architecture 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
Blockchain provides a secure and trusted environment for task offloading in Vehicle Edge Computing (VEC). Nevertheless, single-layer blockchain architecture exhibits low transaction processing efficiency and insufficient scalability in VEC environments. Accordingly, we present 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. Specifically, a Hotstuff–BLSPBFT two-layer blockchain is constructed by employing Hotstuff for the lower-layer blockchain and BLSPBFT for the upper-layer blockchain, thereby improving transaction efficiency of the blockchain system. Then, sharding is performed on consensus nodes of the lower-layer blockchain, and an improved K-Means algorithm is adopted for grouping optimization. Furthermore, utility functions of user vehicles and Road Side Units (RSUs) are formulated based on a two-layer blockchain. A game-based Deep Reinforcement Learning (DRL) model, equipped with two Soft Actor-Critic (SAC) agents, is developed to optimize user and RSU utility functions. The model determines the optimal task offloading strategies and transaction pricing policies, while jointly optimizing the number of consensus node groups and the quantity of nodes within each group to maximize the utility function. Experimental results validate that the proposed scheme not only ensures the security of task offloading but also significantly enhances overall system efficiency.
Guoling Liang, Feng Zhao, Chunhai Li et al.· Mathematics· 0 citations
Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.
K. Jajulwar, Priya Dasarwar, Uma Shankar Yadav et al.· Engineering, Technology &...· 0 citations
The problem of self-interested nodes remains a major challenge in mobile ad hoc networks (MANETs), as individual nodes tend to prioritize the conservation of limited resources such as power and bandwidth at the expense of ensuring global network connectivity. This behavior negatively impacts network efficiency, leading to reduced reliability, a lower packet delivery ratio (PDR), and increased communication latency.
This study proposes an innovative decentralized framework—Tokenized Mobile Semantic Smart Mesh Networks (TMSSMN)—that integrates blockchain-based tokenization with a Knowledge-Underpinned Layer (KUL) to enable incentive-based intelligent routing. The proposed system introduces a pay-per-hop mechanism, where nodes earn rewards through secure micropayments managed by smart contracts for packet forwarding. This approach ensures fairness, transparency, and trust without relying on a central authority. Furthermore, the KUL optimizes routing decisions by integrating semantic insights and contextual data, allowing for more informed, context-aware path selection.
Keywords: MANET, Blockchain, Tokenization, Incentive-based Routing, Smart Contracts, Knowledge-Underpinned Layer (KUL), Cooperative Routing, Packet Delivery Ratio.
Nureddin A. F. Aldali· International Science and Te...· 0 citations
The Internet of Vehicles (IoV) enhances road safety and supports autonomous driving through real-time communication, but current methods face key challenges: rigid resource allocation due to static architectures, communication failures in low-signal areas from infrastructure reliance, and passive defense mechanisms struggle to counter coordinated attacks, while high-latency encryption algorithms further compromise framework real-time performance. To address this, we propose a blockchain-based dynamically adaptive restructuring framework. It enables real-time IoV cluster restructuring by splitting overloaded IoVs to reduce communication overhead, or merging nearby IoVs to optimize resource utilization. In infrastructure-sparse zones, vehicles establish temporary multi-hop communication links based on relative mobility to ensure continuous connectivity. A multi-layered security mechanism integrates physical validation, event verification, and majority voting, achieving over 95% resistance to data tampering. Compared to Raft, PoS, and PBFT, our framework improves consensus speed by 27.06%–38.56%, and reduces transaction latency by 7%–35%, 15%–54%, and 27%–66%, respectively. It also maintains high robustness under dense traffic, high mobility, and weak signals, offering a proactive, adaptive security paradigm for intelligent transportation frameworks.
Jiawei Shi, Yebo Feng, Konglin Zhu et al.· ACM Transactions on Internet...· 0 citations