GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2506.10665v1
• Date: Thu, 12 Jun 2025 12:55:25 GMT
• Title: GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
• Authors: Davide Maffiola, Stefano Longari, Michele Carminati, Mara Tanelli, Stefano Zanero,
• Abstract summary: GOLIATH is a decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants.<n>We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication.
• Score: 9.535698390424669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust. In this paper, we propose GOLIATH, a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants. Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles. We demonstrate its feasibility in the context of vehicle positioning and traffic information management. Each vehicle participating in the decentralized network shares its position and neighbors' ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it. We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication. We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.

Related papers

• Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses [55.70043755630583]
  vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities.<n>We propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling.<n>We develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions.
  arXiv  Detail & Related papers  (2025-05-19T05:04:48Z)
• VMGuard: Reputation-Based Incentive Mechanism for Poisoning Attack Detection in Vehicular Metaverse [52.57251742991769]
  vehicular Metaverse guard (VMGuard) protects vehicular Metaverse systems from data poisoning attacks.<n>VMGuard implements a reputation-based incentive mechanism to assess the trustworthiness of participating SIoT devices.<n>Our system ensures that reliable SIoT devices, previously missclassified, are not barred from participating in future rounds of the market.
  arXiv  Detail & Related papers  (2024-12-05T17:08:20Z)
• Blockchain-Enabled IoV: Secure Communication and Trustworthy Decision-Making [10.36812720854941]
  The Internet of Vehicles (IoV) enables interactions between vehicles, infrastructure, and the environment. This paper introduces a decentralized framework comprising a primary layer for managing inter-vehicle communication and a sub-layer for securing intra-vehicle interactions.
  arXiv  Detail & Related papers  (2024-09-18T00:56:14Z)
• Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
  Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
  arXiv  Detail & Related papers  (2024-03-29T14:58:28Z)
• Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
  Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
  arXiv  Detail & Related papers  (2024-03-29T12:01:31Z)
• Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey [1.0070449177493677]
  Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS) by facilitating communication between vehicles and infrastructure. This survey paper presents a comprehensive analysis of existing authentication mechanisms proposed for cluster-based VANETs. The integration of secure key management techniques is discussed to enhance the overall authentication process.
  arXiv  Detail & Related papers  (2023-12-20T10:58:43Z)
• Blockchain-Enabled Federated Learning Approach for Vehicular Networks [3.1749005168397617]
  We propose a practical approach that merges two emerging technologies: Federated Learning (FL) and the vehicular ecosystem. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.
  arXiv  Detail & Related papers  (2023-11-10T19:51:18Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation [4.1650513680603884]
  We propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.
  arXiv  Detail & Related papers  (2022-04-07T09:23:30Z)
• Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next Frontier for Intelligent Safe-Driving Assessment [17.926728975133113]
  Securing a safe-driving circumstance for connected and autonomous vehicles (CAVs) continues to be a widespread concern. We propose a novel framework of algorithm-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach.
  arXiv  Detail & Related papers  (2021-04-09T19:08:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.