## 2 Literature
Moreover, a cellular application that runs on the android platform is used to search and then reserve the available parking slot. For that, an IoT prototype using Arduino Uno R3 and RIFD is implemented to detect the parking spaces, which are available close to the position of the end user [17; 36]. Moreover, in [5], an IoT-enabled SP system was simulated using Arduino Uno and Raspberry Pi3 to manage automated multilevel parking architectures, wherein IR sensors are deployed to identify the free parking and are controlled by Arduino Uno, which is installed at each level, and all the parking levels are administered through Raspberry Pi3, which is located on the parking ground level. [PERSON] et al. [36] proposed a wireless parking system that employed wireless sensors to obtain an indication of the vacant parking spots in a parking place. For that, a prototype was built using Raspberry Pi to retrieve parking information from field sensors that were connected with Arduino Uno comprised of a sub-1 GHz CC1101 transceiver and ATmega 328 microcontrollers. The sensors were activated to enable communication through Wi-Fi, which thereby transmitted the parking information to the backend server, which was connected via the \"TRENDnet 150 Mbps Wireless N USB Adapter\". The authors also used a cellular android application, set up with Google Maps, to search for a vacant parking spot; thereby, a user can obtain an indication of a closer parking spot within 2 km. In [37], the authors used a computer vision scheme (i.e., cloud vision API) to identify the car in the parking area by capturing and processing its number-plate images through the Raspberry Pi Camera Module, which has compatibilities with the cloud. Overall, the work performed was considered a fine solution to finding parking slots and ensuring parking security. [PERSON] et al. [33] set up a parking system that comprised an ultrasonic sensor for indoor parking and a magnetic sensor for outdoor parking to detect and locate vacant parking spots. Each parking field was installed with a sensor more and a powerful microcontroller called ATmega128 MCU. Thus, the data collected from the mount sensor, including the sensor ID and USIM (Universal Subscriber Identity Module) ID, were always shared with a controller. A Bluetooth communication module was used to enable communication between each sensor node (vacant) and the user's smart cellular phone. In [38], the authors considered the Travelling Officer Problem and formulated a system with the probability-based model (i.e., a spatial-temporal model) to minimize physical patrolling and to make the processing of infringement notices in a shorter amount of time cost efficient. A dataset or spatial-temporal data were collected from the existing on-street embedded sensors and were processed with the greedy and ant-colony-optimization (ACO) algorithms, which are considered parts of the probability model, for optimal performance estimation and pathfinding. The authors concluded that the conducted work outperformed the parking patrolling and maximized the infringement parking notices. Cybersecurity is not the main concern of either IoT or cloud platforms (for example, to authenticate the real identities of participating nodes during data exchange and ensure data integrity in bidirectional communication) [39; 40]. In [41; 42; 43; 44], detailed literature is available to tackle the issues of security and privacy, in which various solutions have been employed, such as mutual authentication, the OAuth protocol, encryption, blockchain, etc., for numerous applications and systems that function under the IoT and cloud-computing paradigms. Considering the massive IoT platform, where roughly millions of devices are geographically distributed and connected to a cloud server, it is a challenging issue to address cybersecurity on such a heterogeneous platform, where field devices from distinct manufacturers, incompatible communication protocols, data standards, etc., are largely grouped [1].