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Due to the increased number of cars, outdoor parking is one of the critical problems. Moreover, the management of the parking system is also considered a difficult task. Humans, on the other hand, were acclimated to efficiently parking their automobiles by providing them with the precise location of parking in advance of their arrival. As a result of human inefficiency, it was unsuccessful and ultimately increased the compliance cost. As a result of the development of the notion of the Internet of Things. A lot of systems were installed regarding smart parking systems that are decreasing the cost but also contain a huge impact on the reduction of emissions from cars. While it is possible to integrate Internet of Things (IoT) devices into automobiles, such an approach will necessitates the deployment of additional infrastructure, which will raise the cost, and also it is not feasible within current infrastructure configurations. Then there's the fact that CCTV technology is widely available and also small enough to fit into any parking area without being noticeable. In this paper, Convolution Neural Network (CNN) based smart parking system is designed and implemented. The CNN is used to detect vacant and occupied parking spaces through CCTV cameras and provide feedback to the passengers. Furthermore, the proposed approach is using CNR and PKLot datasets for ensuring the effectiveness of the model. This was developed to solve the issues of time, cost, and accuracy with the existing systems. As a result, the proposed model provides excellent results in terms of accuracy. Moreover, it is cost-effective and saves time.

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In metropolitan communication infrastructures a revolutionary technique is emerge known as terrestrial optical wireless communication (OWC), which makes a high-rise building connection is possible. Even with this solution, there are many other problems like the influence of haze and fog in the propagation channel which obstruct and scatter OWC propagation light and consequently led to a big attenuation, due to propagate in temporal, angular and spatial of the light signal. Not to mention the minimum visibility that discourages the implementation of the pointing errors (PE) and tracking system. This present work aims to analyze the interrelation between multiple scattering (dense fog, heavy fog, light fog, heavy haze and light haze) and receiver PE under modified duo-binary return-to-zero (MDRZ) system. We found that PE caused by beam swag is the main controlling factor and industriously minimize the link margin, signal-to-noise ratio (SNR), and raise the bit error rate (BER) when there is an increasing the turbulence strength and the track length. We recommended to guarantee transmitter– receiver alignment by installing a variable field of view (FOV) receiver (a tracking system) to overcome the scattering impact of the fog that make render urban laser communication effective in the presence of PE.