Secure and Efficient Task‐Offloading for Decentralized Vehicular Communication Networks

An online task offloading method based on improved starfish optimization and blockchain

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.

Secure and collaborative computation offloading with dynamic service caching in blockchain-empowered UAV-assisted vehicular networks

Decentralized and Adaptive Internet of Vehicles: A Blockchain-Based Approach

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.

Secure and Adaptive Task Offloading in Mobile Cloud Computing: A Blockchain-Enabled Federated Deep Learning Framework with hybrid Optimization

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

Blockchain-Enabled Tokenized Semantic Mesh Networks for Cooperative MANET Routing

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.

Optimizing Real‐Time Vehicular Communications With a Cross‐Layer Model for Energy‐Efficient Clustering and Blockchain‐Based Congestion Control

Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate real‐time communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and ever‐changing network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestion‐free communication simultaneously. To address these challenges, 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). The M‐BHC algorithm enhances the stability of clusters and counters black‐hole attacks by forming clusters dynamically based on real‐time vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bio‐inspired resource allocation strategy modeled on piranha predation behavior. CL‐MABRC addresses network congestion and security using blockchain‐based verification and cross‐layer routing decisions informed by multi‐attribute metrics. Extensive simulations were conducted with a setting of 100 veh/km 2 . The proposed framework showed significant performance improvements over benchmark protocols, such as optimal security‐aware cluster‐based hybrid geographical and opportunistic routing (OSC‐GOR), enhanced location‐aided ant colony routing (ELAACR), trust‐based multi‐objective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, end‐to‐end delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the M‐BHC, EPOA, and CL‐MABRC algorithms create a robust, energy‐efficient, and secure communication framework for VANETs, enhancing scalability, reliability, and real‐time performance in transportation systems.