In-Vehicle Time-Sensitive Networking with Blockchain-Based Error-Bounded Data Management

Post-Quantum DAG-Based Blockchain for Securing Vehicular Sensor Networks

VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains

Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.

A Threshold Homomorphic Blockchain Architecture for Secure and Scalable IoT Sensor Data Aggregation

Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.

Blockchain-driven secure message dissemination in 5G-enabled SDN-IoV using graph-based byzantine consensus and Merkle tree-BLS authentication

A secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.

Blockchain-Enabled AI-Driven Big Data Analytics for Secure and Scalable IoT Ecosystems

The rapid evolution of Internet of Things (IoT) ecosystems produces large quantities of heterogeneous data streams, resulting in major issues related to security, scalability, privacy, and real-time data analytics. This paper suggests a novel framework of a blockchain-based and AI-oriented big data analytics system that supports secure and scalable IoT-based smart grid environments. The proposed architecture combines data acquisition using IoT, big data processing using a distributed system, intelligent predictions using machine learning techniques such as LightGBM, Random Forest, and XGBoost, and integrity preservation using a blockchain-based system, such as Hyperledger Fabric. In the proposed system, the Energy Efficiency Scores are predicted using advanced regression techniques, and LightGBM performs better with the least MAE of 0.4512, MSE of 0.325, RMSE of 0.5701, and the highest R² of 0.9988. To guarantee data immutability and trust, prediction records are cryptographically hashed using SHA-256 hashing and stored on a blockchain ledger, while sensitive payloads are kept off-chain for privacy preservation purposes. Experimental results show that accuracy is improved, residual variance is reduced, and system stability is improved. This proposed framework successfully leverages decentralized trust, intelligent analytics, and scalable processing to deliver a powerful tool for secure, transparent, and privacy-preserving IoT-based energy management systems.

A Blockchain-Based Network Framework for Privacy Preservation in Smart Cities

The proposed BlockSafeNet framework achieved significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures, providing a positive impact on the SC ecosystem.