A Student

Abstract

Most cutting-edge systems for smart parking aren’t 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 don’t provide a complete proof of payment there can be cases of user disputing the charges rightfully due to system error or wrongly. In addition to that users are worried about their privacy being exposed through the parking system, since at the gate the user number plate and the payment information are recorded and kept inside the system; though a couple of research ideas had sprung on incorporating consortium blockchain for privacy, however issues like increased complexity, reduced throughput also scalability problems raised. Up to now most of the research about smart parking haven’t addressed in details how such events can be remediated. Therefore my research will investigate how the system can be fault-tolerant by defining valid states transitions (exact states only through which the payment is declared successful) and guarantee invariants such as for instance no vehicle is charged twice for the same session, no penalty is issued if payment confirmation exists, no session remains active after verified exit. The system is scheduled to handle token based identification and processing through which the user identity will be disclosed only through certain incidents(safety issues, or theft). And to conclude my research will address the issue of non repudiation by providing a full electronic receipt which will indeed infer transparency of the payments and fines if occurred with explanations.

The proposed methodology will utilize IoT integrated ALPR(which is Automated License Plate Recognition) and a Fog layer to handle data processing locally at the gate, which shall remediate the unstable connectivity issues. The system’s effectiveness evaluation will go through an empirical calibrated simulation model to verify that the defined invariants actually hold true under diverse situations in the parking. The expected results shall include a significant reduction in billing disputes and a more privacy-resilient identification process for a greater user satisfaction.