# IoT-Enabled Vehicle Speed Monitoring System
## 2 Literature Review
In modern civilization, transportation plays a key role in the economic development and success of a country [16; 17]. Today, control systems can manage certain situations, but not very effectively, because they require constant input from humans. There is a need for a system with regular satellite updates so that traffic can be managed smoothly. This could be done using IoT /internet protocols for transmitting data. A unified server must beframed, so all humans can see after a complete traffic review. Diversion routes and waiting times would be calculated based on traffic density. In [18], the authors suggested a new approach for handling a traffic grid that uses an IoT definition. Intelligent traffic controllers are framed with devices such as the PiCamera, Raspberry Pi, IR sensors, and radio frequency identification (RFID). IR sensors is used to determine the traffic density. RFID technology is also utilized to provide a green path (zero traffic) for emergency vehicles. This research suggests two types of machine management: manual (with human intervention) and automated (without human intervention). [PERSON] et al. [19] developed a speed detection system that uses a radar gun or speed gun to calculate a vehicle's speed, and if it exceeds the maximum limit, specifics of the vehicle are transmitted to the nearest control room. Speed is monitored by means of micro-electromechanical system (MEMS)-based sensors. A proposed automatic license plate recognition (ALPR) algorithm uses the Hough transform to detect license plates by tracing the edges of the plates [20]. This transformation is a memory- and time-consuming process. Furthermore, in this wavelength-based technique, high-frequency coefficients are used to precisely detect license plates. Nevertheless, this algorithm does not perform efficiently. [PERSON] et al. [21] presented the use of RADAR for vehicle speed calculation. A RADAR works on the principle of bouncing a radio signal back from a moving object and the receiver receives the reflected signal. The receiver RADAR calculates vehicle speed on the basis of calculated difference in frequency. Given the high cost and susceptibility to target identification error, RADAR has not received widespread implementation for vehicles speed monitoring. [PERSON] et al. [6] presented a genetic programming approach for detecting vehicle speed. [PERSON] et al. [7] used an approach of imaging geometry, blur extent in the image and camera pose to calculate vehicle speed. [PERSON] et al. [8] used an approach of computer vision techniques for speed measurement. These approaches suffer issues such as number plate occlusion, headlight occlusion, and/or being costly. [PERSON] [22] built an intelligent traffic monitoring system that relies on automated detection of vehicles. However, in adverse weather conditions, this automated image-based identification of vehicle license plates has a low recognition rate. Research and design demonstrate that it is feasible and affordable to build an intelligent traffic monitoring system based on the IoT. An intelligent traffic monitoring system based on the IoT has a range of benefits, such as low cost, high reliability, and robustness to adverse weather conditions. Furthermore, it would also have a wide range of uses and would be easy to enforce. In [23; 24], the authors have developed an intelligent automatic luminous control system for streetlights according to the intensity of light in the environment. The system is intelligent enough to identify between day and night to switch mode of the system accordingly. This system has been equipped with surveillance system using cameras to control the traffic on road. This system translates audio-free video into text data to extract knowledge about the vehicle. The model presented here is related to controlling the traffic to avoid traffic jam and congestions. This does not offer the speed monitoring feature which is important to enforce the traffic rules.