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:  2) EDGE COMPUTING IN M-IoT 
1. Abstract of Cited Paper (BIB001):  In this paper, we propose a general complex event processing (CEP) engine aiming for accomplishing at smart IoT edge devices in a fully distributed manner. We introduce a pseudo-source mechanism to cover a wide range of processing and obsolete prerequisite of source-specification at the same time, along with a brand-new event specification language defined to support relation-based processing. Against cloud-based approaches, our behind-edge approach can prevent data overflow and privacy issues, and fully distributed processing can draw the power of the edge devices. To achieve that in a resource-limited edge environment, we formulate an optimization problem of processing task assignment and stream delivery, and propose a fully-autonomous workload distribution mechanism. A large-scale simulation with a realistic smart-building scenario shows that our proposed method achieves about 6.6 times smaller flow volume and 2 times lower loss rate compared to centralization and is relatively superior to a hop-based distribution approach. Notably, a prototype engine is successfully deployed over an ad-hoc wireless sensor and actuator network through Intel Edison modules in the real environment. 
2. Abstract of Cited Paper (BIB002):  Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions. 
3. Abstract of Cited Paper (BIB003):  We investigate the design and implementation of Where's The Bear (WTB), an end-to-end, distributed, IoT system for wildlife monitoring.WTB implements a multi-tier (cloud, edge, sensing) system that integrates recent advances in machine learning based image processing to automatically classify animals in images from remote, motion-triggered camera traps.We use non-local, resource-rich, public/private cloud systems to train the machine learning models, and ``in-the-field,'' resource-constrained edge systems to perform classification near the IoT sensing devices (cameras).We deploy WTB at the UCSB Sedgwick Reserve, a 6000 acre site for environmental research and use it to aggregate, manage, and analyze over 1.12M images.WTB integrates Google TensorFlow and OpenCV applications to perform automatic classification and tagging for a subset of these images.To avoid transferring large numbers of training images for TensorFlow over a low-bandwidth network linking Sedgwick to the public/private clouds, we devise a technique that uses stock Google Images to construct a synthetic training set using only a small number of empty, background images from Sedgwick.Our system is able to accurately identify bears, deer, coyotes, and empty images and significantly reduces the time and bandwidth requirements for image transfer, as well as end-user analysis time, since WTB automatically filters the images on-site. 
4. Abstract of Cited Paper (BIB004):  As one of the most striking research hotspots in both academia and industry, Internet of Things (IoT) has been constantly changing our daily life by joining together nearly all we can imagine. From home furnishings and vehicles to urban facilities, all these smart things need powerful managing and processing capabilities to deal with mass multimedia data in different content forms such as images, audios, and videos. Nowadays, since Moore’s Law is no longer applicable, conventional thinking may not be adequate in facing the explosive growing amount of data. Hence, in this paper, we adopt the idea of in-memory processing to solve the problem of real-time multimedia big data computing in IoT. We apply closed-loop feedback in the scheduling method design to integrate in-memory storages of all devices within a 3-tier network structure. In addition, we consider the respective conditions of different real-time required levels and content forms. The analysis results show that our scheduling method can achieve better workload allocation with less latency in comparison of existing methods. 
5. Abstract of Cited Paper (BIB005):  Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT. 
6. Abstract of Cited Paper (BIB006):  Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision, and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves suboptimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.

Literature Review:
Communication between M-IoT devices, edge devices, and cloud incur additional delay if the tasks are not properly scheduled. Xu et al. BIB005 introduced the concept of optimizing in-memory processing to reduce the delay and latency of the network. Three-tier architecture is proposed to formulate closed-loop feedback scheduling and prioritization model to integrate memories of all the edge devices to balance workload allocation. Results depict improvement in latencies for M-IoT systems and rational workload allocation. Genetics a geo-distributed real-time stream processing for the dynamic edge-cloud network is proposed in BIB001 to achieve low latency by exploiting minimum bandwidth allocation and task sharing. A distributed Complex Event Processing engine (CEP) for a fully distributed edge IoT network is presented in BIB002 . CEP utilizes both events based and streambased approaches. The authors proposed a heuristic assignment model for task distribution for a balanced workload between edge devices and cloud. Results show 6.6 times less data volume flow as compared to a centralized framework. Li et al. BIB006 proposed CNN based deep leaning optimization problem to maximize the number of tasks for edge devices subject to limited bandwidth and system capacity. The authors aim to reduce the network traffic and overheads from M-IoT devices to the cloud. The system is designed for online and offline video processing, as video processing is the integration of computer vision and image processing. Sharma and Wang BIB003 devised a collaborative edge-cloud framework. The framework utilizes the historic cloud computing data to aid edge computation to analyze live data for maintaining QoS. The authors also highlighted the issues, challenges, and limitations of both edge and cloud computing. Furthermore, the motivation for a collaborative edgecloud framework is also outlined. Long et al. in BIB007 proposed an edge M-IoT framework to process video on edge devices to improve the accuracy of human detection in surveillance systems. The proposed framework utilizes D2D communication to transmit the divided and compressed video frames to nearby edge nodes that form cooperative groups to process the video frames transmitted to them in multicast or unicast fashion. The authors deduced that human detection accuracy is positively correlated with the video coding rate and formulates an optimization problem to maximize the average video coding rate subject to deadline and group formulation constraint. Group formulation problem is solved by considering it as Winner Determination Problem (WDP), which states that the video chunks are allocated to the group of edge nodes which maximizes the overall utility. Simulation results validate that the proposed work outperforms no cooperation and an arbitrary model. Elias et al. BIB004 devised Where's The Bear (WTB), an image processing technique for edge computing based on NN to recognize animals in wildlife monitoring systems. The objectives of WTB are to conserve bandwidth and improve accuracy. WTB exploits Google TensorFlow for image recognition and classification, and OpenCV for analysis. Results show that implementing image recognition at the edge of the network where the images are acquired reduces latency and conserves bandwidth.