2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 11293-11307· 0 citations· 38 references
Abstract
Amid the explosive growth of latency-aware and computation-sensitive services, mobile edge computing (MEC) assisted by aerial networks, such as high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), has emerged as an effective solution for providing computational capabilities to regions with sparse terrestrial infrastructure. Nevertheless, aerial networks are highly sensitive to energy cost and inherently constrained in hosting dense computing resources, while the exposed wireless environment renders them particularly vulnerable to attacks from malicious nodes. Consequently, it is imperative to develop effective task scheduling and computing resources management mechanisms that satisfy users quality-of-service (QoS) requirements while minimizing system cost and ensuring network reliability. In this paper, we develop a multi-cell MEC network composed of multiple UAVs and multiple HAPs, and further propose a dual-layer blockchain-enabled, federated election (FE)-based (DBFE) group relative policy optimization (GRPO) algorithm to jointly reduce the task offloading latency and system energy expenditure. In particular, blockchain techniques enhance system resilience against non-Byzantine failures, whereas the FE mechanism suppresses the influence of Byzantine behaviors. Simulation results demonstrate that, compared with existing approaches, the proposed method reduces the overall system cost by 19% and 31% under scenarios without malicious nodes and with malicious nodes, respectively.
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.
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations
Simulation results suggest that the proposed EGWO-CS-based approach offers significant improvements in packet delivery ratio, energy efficiency, trust precision, and network lifetime compared to conventional GWO, GWO-CS, and other standard schemes.
Cailing Cheng, Hui Zhang, Li Fei et al.· Computing· 0 citations
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.
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
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