20.500.12556/RUL-132831 Crowdsourced traffic event detection and source reputation assessment using smart contracts 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. truth discovery road traffic event detection reputation assessment blockchain smart contract odkrivanje resnice v podatkih cestni promet detekcija dogodkov ocenjevanje ugleda veriženje podatkovnih blokov pametne pogodbe true false true Angleški jezik Slovenski jezik Članek v reviji 2021-11-04 11:00:17 2021-11-04 11:00:18 2022-09-12 04:13:56 0000-00-00 00:00:00 2019 0 0 17 str. iss. 15, art. 3267 Vol. 19 Aug. 2019 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004:351.811 1424-8220 10.3390/s19153267 12587860 RAZ_Mihelj_Jernej_2019.pdf RAZ_Mihelj_Jernej_2019.pdf 1 68DAD177EF95B9AA602CF4D1D8973C58 d1cb456fb54a9b7befb6a535ed0216e7f4512432001273bd0e35ca422a34ac63 635d02ad-3d56-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150552 https://www.mdpi.com/1424-8220/19/15/3267 1 f80bc002-3d55-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150551 Fakulteta za elektrotehniko 0 0 0