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:  1) FOG COMPUTING IN M-IoT 
1. Abstract of Cited Paper (BIB001):  The recent rapid development of urbanization and Internet of things (IoT) encourages more and more research on Smart City in which computing devices are widely distributed and huge amount of dynamic real-time data are collected and processed. Although vast volume of dynamic data are available for extracting new living patterns and making urban plans, efficient data processing and instant decision making are still key issues, especially in emergency situations requesting quick response with low latency. Fog Computing, as the extension of Cloud Computing, enables the computing tasks accomplished directly at the edge of the network and is characterized as low latency and real time computing. However, it is non-trivial to coordinate highly heterogeneous Fog Computing nodes to function as a homogeneous platform. In this paper, taking urban traffic surveillance as a case study, a dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision making. Furthermore, we have explored the potential to enable multi-target tracking function using a simpler single target tracking algorithm. A prototype is built and the performance is evaluated. The experimental results show that our scheme is a promising solution for smart urban surveillance applications. 
2. Abstract of Cited Paper (BIB002):  With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve quality of service (QoS) in IoT networks is becoming a challenging problem. Currently most interaction between the IoT devices and the supporting back-end servers is done through large scale cloud data centers. However, with the exponential growth of IoT devices and the amount of data they produce, communication between "things" and cloud will be costly, inefficient, and in some cases infeasible. Fog computing serves as solution for this as it provides computation, storage, and networking resource for IoT, closer to things and users. One of the promising advantages of fog is reducing service delay for end user applications, whereas cloud provides extensive computation and storage capacity with a higher latency. Thus it is necessary to understand the interplay between fog computing and cloud, and to evaluate the effect of fog computing on the IoT service delay and QoS. In this paper we will introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay. 
3. Abstract of Cited Paper (BIB003):  Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users’ QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price. 
4. Abstract of Cited Paper (BIB004):  The emerging fog paradigm has been attracting increasing interest from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many fog applications, such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the cloud, but have not been comprehensively investigated in the context of fog architecture. In this article, we present the general models and architecture of fog data streaming, by analyzing the common properties of several typical applications. We also analyze the design space of fog streaming with the consideration of four essential dimensions (system, data, human, and optimization), where both new design challenges and the issues that arise from leveraging existing techniques are investigated, such as cloud stream processing, computer networks, and mobile computing.

Literature Review:
Extending the computing closer to the edge or access network raises several issues and challenges. Determining and classifying critical multimedia data is necessary for load balancing on fog node and the cloud for optimal efficiency. Excessive workload on fog node increases the power consumption, causing efficiency degradation. Deng et al. BIB001 formulated a primal problem for maximizing power conservation in fogcloud hybrid systems. Four layered fog-cloud frameworks have been proposed. Moreover, the authors decompose the problem into three subproblems by specifying the tradeoff between latency and power consumption. The problem is subject to various constraints: 1) delay at fog node, 2) Wide Area Network (WAN) communication system between fog and cloud server, 3) bandwidth and network overheads at the cloud server. Simulation results show that the proposed framework improves latency and power consumption for delay-sensitive traffic. Chen et al. BIB002 devised a fog-based video surveillance system to monitor a speedy road traffic system in urban areas using a drone camera. The authors proposed a tracking algorithm that extracts and transmits image frames of interest to fog node to meet minimum computation and delay requirements. The tracking algorithm utilizes Bayes estimation and probabilistic Monte Carlo simulations. Yousefpour et al. in BIB003 presented a fog-cloud hybrid generic framework to minimize the service latency by sharing the load among fog nodes. The framework considers the queue length and the type of service requested, as the different application requires different service time. The Fog layer offers collaborative architecture to process a request exploiting the concept of load sharing by offloading the service request to neighbor fog nodes. Authors also put forward an approach to find the best fog node to process the request in minimum time by using a centralized fog node for maintaining a reachability table to offload the request to the node which offers minimum service delay. Minimum service delay and propagation delay are measured using a recursive analytical process. Simulation results depict the proposed model outperforms No Fog Processing (NFP). Ni et al. BIB004 put forward a fog computation resource allocation strategy based on Priced Timed Petri Nets (PTPNs) that considers price and time cost of task completion. The proposed scheme utilizes a dynamic recursive algorithm for fog resource allocation. The authors also presented a probabilistic model to predict the time cost of task completion. The framework provides a novel scheme for users to select the resource pool that fulfills their requirements. The innovation can still be improved by implementing the ML technique to train the system by mapping the nature of services with required fog resource and service time. Yang in BIB005 discussed fog architectures for data stream processing in four different applications: 1) IoT stream analytics, 2) real-time crowdsourcing, 3) network control, 4) event processing. The author investigates various issues and challenges in applications focused on real-time monitoring and crowdsourcing. The main objective of this architecture is to incorporate fog computation in the M-IoT network that augments the network throughput, latency, fairness, stability, and reliability.