You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Multimedia Internet of Things: A Comprehensive Survey 
Chapter Title:  A. ROAD MANAGEMENT SYSTEM 1) TRAFFIC MONITORING 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
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