A context‐aware framework for modelling and verification of smart parking systems in urban cities

Parking spaces have been considered as vital resources in urban areas. Finding parking spaces in jam‐packed areas is often challenging, stressful, and uncertain for the drivers that causes traffic congestion with a consequent of wastage of time, fuel, and increase of pollution. In recent years, context‐aware computing paradigm has been considered to be the most effective approach to address these kinds of issues. Context‐aware systems acquire and understand contextual information according to the current situation, perform reasoning, and then act intelligently on behalf of the user. These applications often run on tiny resource‐bounded smart devices with the incorporation of embedded or attached sensors on these devices and they often exhibit complex and adaptive behaviour. In this paper, we propose a context‐aware parking application framework to assist drivers in finding parking slots dynamically while moving and/or arriving at the destination. We optimize the context‐aware parking framework with bounds on computational resources for the decision support dynamically in a highly decentralized environment. To illustrate the use of the proposed system, we model the context‐aware parking system using Uppaal model checker for formal analysis and verify the correctness properties of the system.