An edge computing and distributed ledger technology architecture for secure and efficient transportation

Author(s)

Moura, Douglas L. L. ; Aquino, Andre L. L. ; Loureiro, Antonio A. F.

• Document type: Journal article
• Source: Ad Hoc Networks; v. 164, p. 14-pg., 2024-08-28.

Abstract

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture's effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization. (AU)

• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 23/00721-1 - Quantifying uncertainty in adversarial federated learning
• Grantee: Heitor Soares Ramos Filho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS)
• Grantee: Antonio Alfredo Ferreira Loureiro
• Support Opportunities: Research Projects - Thematic Grants