Related papers: B-FERL: Blockchain based Framework for Securing Sm…

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy,…

With the rapid development of the Internet of Things (IoT) and its potential integration with the traditional Vehicular Ad-Hoc Networks (VANETs), we have witnessed the emergence of the Internet of Vehicles (IoV), which promises to…

Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative…

Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most…

A blockchain framework is presented for addressing the privacy and security challenges associated with the Big Data in smart mobility. It is composed of individuals, companies, government and universities where all the participants collect,…

This paper introduces a novel blockchain-enabled authentication and communications network for scalable Internet of Vehicles, which aims to bolster security and confidentiality, diminish communications latency, and reduce dependence on…

Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL,…

In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most…

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with…

Infrastructure maintenance is inherently complex, especially for widely dispersed transport systems like roads and railroads. Maintaining this infrastructure involves multiple partners working together to ensure safe, efficient upkeep that…

The Internet of Vehicles (IoV) can significantly improve transportation efficiency and ensure traffic safety. Authentication is regarded as the fundamental defense line against attacks in IoV. However, the state-of-the-art approaches suffer…

Data driven approaches to problem solving are, in many regards, the holy grail of evidence backed decision making. Using first-party empirical data to analyze behavior and establish predictions yields us the ability to base in-depth…

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…

Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in…

Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and…

The automotive industry has seen an increased need for connectivity, both as a result of the advent of autonomous driving and the rise of connected cars and truck fleets. This shift has led to issues such as trusted coordination and a wider…

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to…

The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles.…

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from…

With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns…