This paper addresses the fundamental problem of data explosion encountered in the Deep learning-based Direction of Arrival (DOA) estimation, which is caused by the fact that the amount of training data grows exponentially with the number of sources. The data explosion enforces harsh requirements on the computational resources to train the deep-learning (DL) model, even making the training of the DL model mission impossible. To address this, we analyze the similarity between the received array signals in the cases of different sources, which serves as the basis for the proposed training framework. We then give the explicit formula of all possible angle combinations, demonstrating the training data explosion issue. Afterwards, we propose the training framework for progressively fine-tuning the model as the number of sources increases. By reusing a pre-trained model for$k-1$sources and fine-tuning it with a small portion of the data of$k$sources, the method significantly reduces the training overhead. The core of this method lies in utilizing the signal features already learned by the model trained with fewer sources and adapting it to the higher-dimensional source scenario through fine-tuning, thus avoiding the data redundancy associated with training from scratch. Numerical results show the model using the proposed training framework with only 1% of the data achieves similar performance as the fully trained model with 100% of the data.

Autonomous vehicles (AVs) can sense their environment and navigate without human input. However, when AVs are involved in accidents with other AVs or human subjects, liability must be indisputably determined based on accident forensics. However, existing methods rely on centralized authorities to collect and manage accident data, making them vulnerable to cybersecurity attacks (e.g., tampering, denial-of-service). Decentralized systems, such as blockchain networks, present a promising alternative; however, their data immutability properties present challenges in practice. Consequently, this paper introduces a redactable blockchain solution for vehicular event forensics. This novel approach, underpinned by a decentralized attribute-based chameleon hash function, supports modifying data from multiple blocks, as well as individual transaction-level modification operations, without the issue of a single point of failure. We implemented the proposed system using the Charm cryptography library and the Hyperledger Fabric blockchain platform. The Chameleon hash function can support redactable operations for on-chain vehicular event records with a latency of seconds. We also conducted extensive experiments on blockchain performance and the witness group formation cost, highlighting that the proposed redactable blockchain is efficient in practice for vehicular event forensics tasks.

The nonfungible tokens (NFTs) are unique cryptocurrencies that exist on a blockchain and cannot be replicated. However, today's NFT market lacks a sensible pricing framework, which causes NFT price fluctuations to interfere with the investment market. The purpose of this research is to build a dynamic pricing mechanism for NFT based on NFT features, validate the improvement of factor analysis on the pricing model, and integrate the rarity-based model with market factors in an online market. We presented and implemented a prototype pricing algorithm through a designed NFT market. This implementation shows that this advanced pricing mechanism could be used in a real-world pricing scenario and could provide a reasonable price change when market factors change. Furthermore, the experimental results demonstrated that the rarity scores of the features and market factors can potentially induce price fluctuations within the range of negative 0.4 to positive 0.4.

Managing urban traffic dynamics is critical in Intelligent Transportation Systems (ITS), where short-term traffic prediction is vital for effective congestion management and vehicle routing. While existing centralized deep learning (DL) models have achieved high prediction accuracy, their applicability is limited in decentralized ITS environments. The increasing use of connected vehicles and mobile sensors has led to decentralized data generation in ITS, presenting an opportunity to improve traffic prediction through collaborative machine learning. Recently, blockchain technology has shown promise in improving ITS efficiency, security, and reliability. In conjunction with blockchain, Federated Learning (FL) is a suitable approach to leverage online data streams in ITS; however, most research on FL for traffic prediction focuses on offline learning scenarios. This paper researches a blockchain-enhanced architecture for training online traffic prediction models using FL. The proposed approach enables decentralized model training at the edge of the ITS network, and extensive experiments used dynamically collected arterial traffic data shards as a case study to evaluate online learning performance. The results demonstrate that our online FL approach outperforms the per-device, non-federated baseline models for most sensors while maintaining a suitable execution time and latency for real-world deployment.

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensure vehicular data integrity, and guard platoons against potential attackers in mixed fleet environments, verifying any given vehicle’s identity information before granting it access to join a platoon is pivotal. Besides, due to privacy concerns, truck owners may be reluctant to disclose private vehicular information, which can reveal their business data to untrusted third parties. To address these issues, this is the first study to propose an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in truck platooning. We provide the correctness proof and the security analysis of our proposed authentication scheme, highlighting its increased security and fast performance. The platooning formation procedure is re-designed to seamlessly incorporate the proposed authentication scheme, including the 1st catch-up and cooperative driving steps. The blockchain performs the role of verifier within the authentication scheme and stores platooning records on its digital ledger to guarantee data immutability and integrity. In addition, the proposed programmable access control policies enable truck companies to define who is allowed to access their platoon records. We implement the proposed system and perform extensive experiments on the Hyperledger platform. The results show that the blockchain can provide low latency and high throughput, the aggregated approach can offer a constant verification time of 500 milliseconds regardless of the number of proofs, and the platooning formation only takes seconds under different strategies. The experimental results demonstrate the feasibility of our design for use in real-world truck platooning.

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients’ anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA’s signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios’ requirements while safeguarding EHR and is robust against unauthorized retrievals.

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating existing consensus protocols in many IoT systems limits scalability and leads to high computational cost and consensus latency. In addition, location-centric characteristics of many IoT applications paired with limited storage and computing power of IoT devices bring about more limitations, primarily due to the location-agnostic designs in blockchains. We propose a hierarchical and location-aware consensus protocol (LH-Raft) for IoT-blockchain applications inspired by the original Raft protocol to address these limitations. The proposed LH-Raft protocol forms local consensus candidate groups based on nodes’ reputation and distance to elect the leaders in each sub-layer blockchain. It utilizes a threshold signature scheme to reach global consensus and the local and global log replication to maintain consistency for blockchain transactions. To evaluate the performance of LH-Raft, we first conduct an extensive numerical analysis based on the proposed reputation mechanism and the candidate group formation model. We then compare the performance of LH-Raft against the classical Raft protocol from both theoretical and experimental perspectives. We evaluate the proposed threshold signature scheme using Hyperledger Ursa cryptography library to measure various consensus nodes’ signing and verification time. Experimental results show that the proposed LH-Raft protocol is scalable for large IoT applications and significantly reduces the communication cost, consensus latency, and agreement time for consensus processing.