Mihelj Jernej, Zhang Yuan, Kos Andrej, Sedlar Urban
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia.
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China.
Sensors (Basel). 2019 Jul 25;19(15):3267. doi: 10.3390/s19153267.
Real-time data about various traffic events and conditions-offences, accidents, dangerous driving, or dangerous road conditions-is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.
有关各种交通事件和状况（违法行为、事故、危险驾驶或危险路况）的实时数据对于安全高效的交通运输至关重要。与范围和数量通常有限的路边基础设施数据不同，众包感知方法有望更广泛、全面地覆盖交通事件。然而，为确保安全高效的交通运行，评估众包数据的可信度至关重要；这还包括检测故意或无意的操纵、欺骗和垃圾信息。在本文中，我们设计并展示了一种针对不可靠数据源的道路交通事件检测和源信誉评估系统。特别注意使该系统适应分散模式运行，在图灵完备的区块链平台上使用智能合约，消除对此类系统的单一权威控制，并增强对机构数据操纵的抵御能力。使用合成交通事件数据集和从真实用户收集的数据集对所提出的解决方案进行了评估，这些数据是通过在用于驾驶员培训的专业驾驶模拟器中的交通事件报告移动应用程序收集的。结果表明，所提出的系统能够准确检测一系列操纵和错误报告行为，并且即使在区块链平台虚拟机的资源受限环境中也能快速收敛到最终信任分数。
