CORTEXA
← Browse
crossrefFuture Internet2024-12-18Cited by 12

Enhancing Autonomous Vehicle Safety with Blockchain Technology: Securing Vehicle Communication and AI Systems

Stefan Iordache, Catalina Camelia Patilea, Ciprian Paduraru

In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles.

View free PDFSource page

Related papers

crossrefFuture Internet2024-12-02Cited by 12

Advances in Blockchain-Based Internet of Vehicles Application: Prospect for Machine Learning Integration

Emmanuel Ekene Okere, Vipin Balyan

Blockchain-based technology has completely revolutionized the development of the Internet of Vehicles (IoV) framework. This has led to increasing blockchain-based Internet of Vehicles application over the last decade. However, challenges persist, including scalability, interopera…

View free PDFSource page
crossrefFuture Internet2024-09-06Cited by 27

Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

Elias Dritsas, Maria Trigka

The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive c…

View free PDFSource page
crossrefFuture Internet2024-05-17Cited by 14

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Roilhi F. Ibarra-Hernández, Francisco R. Castillo-Soria, Carlos A. Gutiérrez, Abel García-Barrientos, Luis Alberto Vásquez-Toledo, J. Alberto Del-Puerto-Flores

Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6…

View free PDFSource page
crossrefFuture Internet2025-10-16Cited by 1

QL-AODV: Q-Learning-Enhanced Multi-Path Routing Protocol for 6G-Enabled Autonomous Aerial Vehicle Networks

Abdelhamied A. Ateya, Nguyen Duc Tu, Ammar Muthanna, Andrey Koucheryavy, Dmitry Kozyrev, János Sztrik

With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments pr…

View free PDFSource page
crossrefFuture Internet2024-06-27Cited by 48

Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies

José Cunha, Pedro Ferreira, Eva M. Castro, Paula Cristina Oliveira, Maria João Nicolau, Iván Núñez, et al.

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same p…

View free PDFSource page
crossrefFuture Internet2025-07-18Cited by 14

Enhancing Security in 5G and Future 6G Networks: Machine Learning Approaches for Adaptive Intrusion Detection and Prevention

Konstantinos Kalodanis, Charalampos Papapavlou, Georgios Feretzakis

The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastr…

View free PDFSource page