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Smart City is the product of accelerated development of the new generation information technology and knowledge-based economy, based on the network combination of the Internet, telecommunications network, broadcast network, wireless broadband network and other sensors networks where Internet of Things technology (IoT) as its core. Traffic congestion caused by vehicle is an alarming problem at a global scale and it has been growing exponentially. Searching for a parking space is a routine (and often frustrating) activity for many people in cities around the world. This search burns about one million barrels of the world's oil every day. A smart Parking system typically obtains information about available parking spaces in a particular geographic area and process is real-time to place vehicles at available positions. In our proposed chapter, we will be discussing the advanced features of IoT in order to development an intelligent parking management system in a smart city i.e. called as Smart Parking system.

We consider simple models of congestion control in high-speed networks and develop diffusion approximations which could be useful for resource allocation. We first show that, if the arrival process is Poisson and the service times are exponential, then, under a certain scaling, the steady-state distribution of the number of sources in the system consists of appropriately normalized and truncated Gaussian and exponential distributions. We then consider the case where the arrival process is a general renewal process with finite coefficient of variation and service-time distributions that are phase-type, and show the impact of these distributions on the steady-state distribution. We use these results to relate the capacity of a bottleneck node to performance measures of interest for best-effort traffic, such as the mean file transfer time or probability of congestion.