No abstract

Summary Parking vehicles in densely populated areas are often challenging, stressful, and sometimes it becomes a monotonous job for the drivers in jam‐packed areas. There are several reasons for the delay in finding parking spaces such as scarcity of parking slots, disordered or unmanaged parking of vehicles, lacking or unaware of parking information at the destination, which further leads to the wastage of time, fuel, energy and increase in environmental pollution. Literature has revealed a significant number of smart parking solutions based on the Internet of Things (IoT) and context‐awareness with the incorporation of routing strategies and vehicle detection techniques in a pervasive computing environment. With the rapid escalation of the smart and intelligent devices along with their applicability in a highly decentralized environment, real‐time traffic monitoring, and finding parking spaces have become quite trivial. Smart parking sensors and technologies assist drivers in finding vacant parking slots while they are on the way to their destination. Considering the needs, wants, and demands of metropolitan cities, in this article, we have reviewed the recently published articles, mostly from the last 5 years, on smart parking systems augmented with sensors, embedded systems, context‐awareness capability, and IoT which yields in saving time, fuel, energy, and reduces the stress of the drivers. To accomplish this, we have reviewed different models on smart parking solutions based on algorithmic formalisms, theoretical frameworks, formal models, smart device‐based prototypes as well as real‐time applications, and verifying the correctness properties of the system. The results shown may provide a base for the state of the art future research directions.

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.