Multimedia Internet of Things: A Comprehensive Survey <s> A. ROAD MANAGEMENT SYSTEM 1) TRAFFIC MONITORING <s> In this paper, we present a comprehensive methodology to support IoT based semantic traffic monitoring in smart cities to derive the status of traffic on a road segment in order to optimize traffic flow during rush hours. Smart traffic monitoring involves real-time traffic scene analysis from CCTV cameras to detect and identify stalled vehicles and measure traffic density on roads. The scene analysis essentially necessitates tracking and counting the number of moving and stopped vehicles as they progress along the image sequence by utilizing computer vision techniques. The obtained features along with manually annotated traffic data are translated to a higher level information using semantic web technologies, thus providing a common platform to exchange the information. As the perceptual modelling of traffic data involves media patterns so we have employed Multimedia Web Ontology Language (MOWL) for the semantic interpretation and to tackle inherent uncertainties involved in these media documents. MOWL supports hierarchical probabilistic reasoning framework and utilizes time-varying Dynamic Bayesian Networks (DBN) to perform semantic comparison and predict the evolution of changing situations. In this paper, we discuss the key tasks of vision and probabilistic reasoning components that provide a feasible solution to identify the cause of traffic jam. The proposed approach can be used to trigger an automatic warning or alerts to traffic authorities in case any anomalous activity is encountered and guide users to take some different route to avoid jam. The experimental results shows effectiveness of real-time vehicle monitoring to assess congestion on road and offer user an assistive environment to operate. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. ROAD MANAGEMENT SYSTEM 1) TRAFFIC MONITORING <s> Traffic Congestion and traffic monitoring is one of the important problems all over the world. This work uses IOT and Adaptive Neuro Fuzzy Inference System (ANFIS) to improve traffic conditions. An ANFIS traffic light controller with inputs as waiting time and vehicle density is developed using MATLAB SIMULINK environment. A camera is used to capture the traffic scenes and this image is transferred to the cloud using Arduino UNO and ThingSpeak Platform. The image is then analyzed in the server using ANFIS controller and appropriate control signals are sent to the traffic signals. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. ROAD MANAGEMENT SYSTEM 1) TRAFFIC MONITORING <s> With an ever-increasing population growth in cities around the world, continuous production of all kinds vehicles by manufacturers, and the number of vehicles on the roads will only continue to rise. This naturally leads to increased traffic congestion, especially in large metropolitan areas and even more so during peak rush hour time. This phenomenon constantly puts pressure on researchers, city officials, and urban planners to continue to improve traffic management systems in ways that are safer and economically more efficient. In order to address this evolving problem, a number of studies have been conducted that have resulted in some notable improvements such as designated lanes for emergency vehicles in urban areas. However, even with these lanes, often the ideal target-times for emergency vehicles to reach their destinations is hard to achieve. A new method which seeks to address this issue is called Intelligent Transportation System (ITS). This method can help solve the problem by integrating existing technology with the current infrastructure. In this paper, our goal is to compare different methods for managing traffics, namely Traffic Light Systems (TLS): Static and Dynamic TLS, Radio Frequency Identification (RFID), and Internet of Things (IoT). In the latter method, traffic data are quickly acquired and sent to Big Data for processing and mobile applications AKA User Interface (UI) to estimate traffic density in various areas in order to suggest alternative ways to alleviate traffic. <s> BIB003
Efficient traffic monitoring and control is one of the major issues in the smart city. To handle this problem, there are different solutions based on infra-red detectors, magnetic loops, and microwave radars. These conventional techniques incur huge installation, maintenance costs, and lack of accuracy. The researcher put forward M-IoT based techniques to effectively detect and identify the volume of traffic and predict the reason for a traffic jam. Fig. 5 conceptualize the M-IoT to enhance the road management system. Authors in BIB001 presented computer vision-based ontology-driven contextaware M-IoT architecture to estimate the traffic by tracking the number of vehicles present on the road in real-time from CCTV camera video and segregate moving vehicles from stationary vehicles. Based on the density of stationary traffic that occupies the road is compared to a threshold value. Multimedia Web Ontology Language (MOWL) has been adopted, which utilizes time-varying Dynamic Bayesian Networks (DBN) for comparison and predicts the cause of traffic congestion. Automatic alerts can be triggered by road traffic authorities to avoid a traffic jam. Raspberry Pi and Pi camera-based IoT architecture are presented in  for traffic surveillance and road accidents. The authors used a Gaussian Mixture Model (GMM) with canny edge detection on VIRAT and MIT traffic data set to evaluate their proposal. The article also presents several surveyed efficient methods, i.e., hidden Markov and neural network, Lucas-Kanade, and K-means clustering techniques for accurate accident detection and vehicle tracking. George et al. BIB002 proposed an Adaptive Nero Fuzzy Inference System (ANFIS) and image processing based technique using M-IoT for the better traffic light management and monitoring system. Camera images are obtained and analyzed on a ThinkSpeak based cloud server. Based on the analysis, control actions are given to traffic lights. Machine vision blob analysis technique is used to detect and locate the vehicle. Due to poor illumination conditions during night, the Otsu image processing technique is used. Low Power Wide Area Network (LoPWAN) and Long Range (LoRa) receiver and transmitters are adapted to propose IoT smart traffic monitoring and control architecture in  . A comparative survey on the M-IoT based traffic management system is presented in BIB003 . VOLUME 8, 2020 