Blockchain Traffic Offence Demerit Points Smart Contracts: Proof of Work

Abstract

In Malaysia, a new regulation of traffic offences demerit points has been over a debate. Therefore, a blockchain model is formulated to solve this issue. It serves a purpose to be a Proof of Work (PoW) of a blockchain system. This model contains application layer and blockchain layer with smart contract inside. The smart contracts act as a conditional filter which follows the regulation rules. It contains three contracts starting from the declaration of each offence’s demerit points and fines until the penalties when a certain amount of demerit points is collected, including revocation of driver license. The contracts will be automatically executed when such conditions are fulfilled. A transaction schema is also designed to match the schema of a traffic offence system. This model is deployed in online environment with two servers synced to each other to prove the decentralized characteristic of blockchain. It is developed using NodeJS while preserving JSON format for transaction between server and client. A user interface is also provided as a simulation media where a traffic officer can input offences and send it to blockchain server while public users or the driver itself can check the status of the driver license recorded on the blockchain. Government officer can monitor the records through a dashboard analytics provided which contains graphs and charts based on the records. This interface is used as media to do evaluation which produces satisfying results. The evaluation shows that the smart contracts are executed properly as compared to real regulations.

Authors and Affiliations

Aditya Pradana, Goh Ong Sing, Yogan Jaya Kumar, Ali A. Mohammed

Keywords

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  • EP ID EP417682
  • DOI 10.14569/IJACSA.2018.091153
  • Views 96
  • Downloads 0

How To Cite

Aditya Pradana, Goh Ong Sing, Yogan Jaya Kumar, Ali A. Mohammed (2018). Blockchain Traffic Offence Demerit Points Smart Contracts: Proof of Work. International Journal of Advanced Computer Science & Applications, 9(11), 375-382. https://europub.co.uk/articles/-A-417682