Provenance-based smart parking system with multilevel fog nodes.

The advent of smart cities and IoT resulted in smart applications like smart traffic, energy management systems, smart parking systems, waste management systems, and public safety systems, which are rapidly expanding. These smart systems significantly address everyday life issues and are very helpful in mitigating the risks involved in establishing smart cities. Smart and environment-friendly transportation can be achieved by reducing traffic jams and parking problems. Many researchers have attempted to automate parking space allocation using cutting-edge technologies such as WSN, cloud computing, and fog computing. This paper addresses the abovementioned issues and introduces a provenance-based smart parking system for reducing traffic jams and parking space availability. The proposed article depicts multilevel fog nodes, namely upper-level fog and lower-level fog nodes, for efficient data storage, transfer, utilization, and real-time availability of resources in parking areas. The provenance component of the system is used to assist users in knowing about the parking areas. Simulation is carried out in iFogSim and evaluated in terms of network usage and latency. The proposed system outperforms others by providing efficient network usage and less latency with limited resources. Experimental results indicate that the proposed provenance-based model outperforms latency reduction and fog environment network usage. The proposed model also implements upper-level and lower-level fog nodes that minimize downtime and enhance the reliability of the fog network.