Ensuring Fault Tolerance and Privacy in IoT-Integrated Smart Parking Systems

Abstract

Most cutting-edge systems for smart parking are not fault tolerant mostly because they handle the efficiency part but slightly overlook the security part, for instances like: the system misses or detects the wrong number plate, duplicate payments occurs, number plate is partially visible(dirty or occluded) mainly due to the system connectivity being unstable, or since the systems already in place do not provide a complete proof of payment there can be cases of user disputing the charges rightfully due to system error or wrongly. In addition, users are concerned about their privacy being exposed through the parking system, as at the gate, their license plate and payment information are recorded and stored. Though a couple of research ideas have emerged for incorporating consortium blockchain for privacy, issues such as increased complexity, reduced throughput, and scalability problems have been raised. Up to now, most of the research about smart parking has not addressed in detail how such events can be remediated. Therefore, our research will investigate how the system can be fault-tolerant by defining valid state transitions (exact states only through which the payment is declared successful) and guarantee invariants such as no vehicle is charged twice for the same session, no penalty is issued if payment confirmation exists, and no session remains active after verified exit. The system is scheduled to handle token-based identification and processing, through which the user's identity will be disclosed only in certain incidents (safety issues or theft). Moreover, to conclude our research, we will address the issue of non-repudiation by providing a full electronic receipt, which will indeed infer transparency of payments and fines, with explanations.

The proposed methodology will utilize IoT-integrated ALPR (Automated License Plate Recognition) and a Fog layer to handle data processing locally at the gate, thereby remedying unstable connectivity issues. The system's effectiveness will be evaluated using an empirically calibrated simulation model to verify that the defined invariants hold true across diverse situations in the parking. The expected results shall include a significant reduction in billing disputes and a more privacy-resilient identification process, resulting in greater user satisfaction.