Forwarding Metrology with an IoT and Blockchain Approach: The Gas Pumps Use Case
Gabriel Estevam, Pedro Henrique de Sena Trombini Taglialenha, Luis Felipe Fabiane, Thaís Bardini Idalino, Martín Augusto Gagliotti Vigil, Jean Everson Martina · Journal of Internet Services and Applications · 2025
Volumetric fraud at gas pumps is a serious and ongoing issue, leading to substantial financial losses for consumers.Recognizing the severity of this problem, regulatory agencies in Brazil have introduced new gas pumps equipped with digital certification. This initiative is part of a broader strategy to integrate digital certification into measuring instruments, starting with gas pumps, as a proactive measure to counteract fraud. Additionally, Brazilian agencies are developing a mobile application that will allow users to access refueling data and perform their own inspections. In this context, we aim to further advance the topic by proposing a system that enhances the smart capabilities of metrology. We introduce the adoption of blockchain technology to establish consumer communities and promote metrology within IoT and cloud computing landscape. Our proposal allows users to share their refueling data, facilitating more active gas pump inspections with less dependence on regulatory agencies. We propose utilizing the user data in an evaluation system that cross-references the data and applies statistical methods to detect both volumetric fraud and fuel tampering. The system is designed to generate a ranking of gas pumps with the highest potential of being fraudulent, providing insights to both regulatory agencies and consumers. By leveraging blockchain technology, we can securely record user data and deliver the evaluation service in a transparent and decentralized manner. Finally, we apply statistical techniques to address the issue of trusting external data incorporated into the blockchain, which is commonly referred to as the oracle problem in the literature. Our simulation results demonstrate that, by using only refueling and vehicle data, we can achieve fraud detection accuracy of 89% in scenarios that closely resemble real-world conditions.