# IoT-Enabled Vehicle Speed Monitoring System
## 1 Introduction  
The Internet of Things (IoT) is a network of electronic devices (usually referred to as things in this context) that communicate with each other through the Internet [1], also sometimes called cyber-physical systems [2]. These devices collect data from the environment and store it in a cloud that can be easily accessed and retrieved whenever needed. Generally, this technology incorporates sensors and actuators to capture and transmit data over the Internet. On-demand service provision, resource sharing, and flexibility are all fundamental features of the cloud-based IoT infrastructure. Smart energy, smart city, health monitoring systems, and smart vehicle monitoring systems are some of the crucial applications of IoT. The present age transportation industry is associated with huge costs from disasters, large numbers of accidents, and injuries that lead to loss of life. All over the world, the major reasons for such accidents, injuries, and loss of life include violating traffic laws against speeding and dangerous driving. Despite the reality that dangerous driving is amajor problem, the current detection system of patrol officers is inefficient [3]. Increased speed multiplies the risks of collisions and injuries. At a higher speed, the driver needs a more distance to stop. The most important requirement is to create a system that can track, report, and record vehicle speeds. According to a World Health Organization (WHO) report [3], over 1.2 million people around the globe each year lose their lives due to road accidents. National Highway & Motorway Police officials in Pakistan lament that, on average, 15,000 to 16,000 people die each year due to traffic accidents, mostly occurring due to speeding and violations of other traffic laws. For ensure secure and safe journeys on motorways, it is necessary to identify areas where speed and traffic violations occur. An isolated speed detection system does not provide such information, therefore resulting in continuously increasing numbers of motorway accidents in Pakistan. Previously, [PERSON] [4] published a method for measuring speed based on license number plates; however, this method has a flaw in that it is hard to distinguish the number plate of the second car when two vehicles appear in a frame following each other. [PERSON] et al. [5] proposed using headlight detection to monitor a moving vehicle and then using the pinhole and Euclidian distance techniques to compute its speed. However, this method has a drawback if two cars appear in the same frame since detecting the rear vehicle becomes difficult. A genetic programming method for detecting vehicle speed was proposed by [PERSON] et al. [6]. [PERSON] et al. [7] calculated vehicle speed using imaging geometry, image blur, and camera posture. For speed measurement, [PERSON] et al. [8], employed a computer vision approach. Fortunately, these issues can be resolved with proper application of the Internet of Things (IoT) targeting risk mitigation, loss prevention, protection, and cost reduction. Cameras integrated with Raspberry Pi (RPi) modules, along with deep-learning analytical tools and image processing algorithms, can gather information and make predictions and decisions that will make roads safer. Building such an IoT-based, intelligent, average speed-monitoring system has a range of advantages, including improving road conditions, reducing and managing costs, and increasing reliability and protections in traffic. This paper develops an IoT-based vehicle speed monitoring system that can provide efficient results in an overcrowded and irregular traffic environment such as Pakistan's traffic situation. The employment of cutting-edge procedures and technologies has made the traffic system safer and more secure for all sorts of vehicles, stopping them from speeding and breaching traffic regulations. Using image processing techniques and RPi modules, we established a linked infrastructure network of IoT with RPi modules deployed for speed checks and accident detection on the high-speed highways.