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: A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective 
Chapter Title:  A. OVERVIEW 
1. Abstract of Cited Paper (BIB001):  Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed. 
2. Abstract of Cited Paper (BIB002):  The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. 
3. Abstract of Cited Paper (BIB003):  This article presents a flexible architecture for Internet of Things (IoT) data analytics using the concept of fog computing. The authors identify different actors and their roles in order to design adaptive IoT data analytics solutions. The presented approach can be used to effectively design robust IoT applications that require a tradeoff between cloud- and edge-based computing depending on dynamic application requirements. The potential use cases of this technology can be found in scenarios such as smart cities, security surveillance, and smart manufacturing, where the quality of user experience is important. 
4. Abstract of Cited Paper (BIB004):  In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission. 
5. Abstract of Cited Paper (BIB005):  The Industrial Internet of Things is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. In this paper, we present a mobile cloud based scheduling strategy for the industrial Internet of Things. Several computing paradigms, such as mobile cloud computing, fog computing, and edge computing can be integrated to the industrial Internet of Things, which allow to offload tasks to the cloud for execution. We model the task scheduling problem as an energy consumption optimization problem, while taking into account task dependency, data transmission, and some constraint conditions, such as response time deadline and cost, and further solve it by genetic algorithms. A series of simulation experiments are conducted to evaluate the performance of the algorithm and the results have shown that our proposal is more efficient than the baseline approach.

Literature Review:
From the computing infrastructure perspective, as machine devices have limited capacity in terms of computation and storage, the deployment of computing infrastructure that can provide big data computing and storage for machine devices and support the computing needs for smart-world systems, including smart manufacturing systems, is paramount. Notice that a centralized architecture limits the applicability of cloud computing to machine devices for two reasons. First, the significant volume of data generated from vast numbers of machine devices in disparate geographical locations will be transmitted to the centralized cloud, which could overload the network infrastructure. Second, the centralized cloud is often located far away from machine devices, which could cause high latency during data transmission. For some smart-world systems (e.g., I-IoT), such a latency becomes unacceptable for data analysis and control decision making BIB001 , BIB002 . Distinct from cloud computing, edge computing tends to leverage the computing and storage capabilities from edge devices (e.g., computing edge nodes). By doing this, edge computing has a great potential to support the computing needs of smart-world systems such as I-IoT. As edge devices are commonly deployed to the locations near to machine devices, some challenges issues (e.g., network overload, high latency) can be addressed BIB003 - BIB004 . Nonetheless, some challenges remain while leveraging edge computing in smartworld systems (e.g., I-IoT). Some challenges include system management, synchronization, security and privacy, and integration with other network technologies, among others BIB003 ,  . Indeed, a variety of relevant research efforts have been conducted in this direction. For example, Li et al. BIB005 addressed the issue of integrating SDN and edge computing in I-IoT, and designed an adaptive transmission architecture. Additionally, Tang et al. BIB006 designed a mobile cloudbased scheduling scheme for I-IoT and investigated the task scheduling problem. In their problem formalization, the energy consumption optimization, as well as a number of factors such as task dependency and constraints such as response time deadline are considered. To solve the problem, genetic algorithms were designed and their effectiveness validated. We use Figure 8 and Figure 9 to illustrate the structures of cloud computing-based architectures and hybrid cloud and edge computing-based architectures for I-IoT, respectively. As shown in Figure 8 , the data generated from components in the system will be gathered and transmitted to the cloud for processing, analysis and storage. In Figure 9 , we can see that both edge devices and cloud computing facilities will be leveraged to handle data in a multi-tier fashion. In addition, we propose a three-dimensional framework to explore the problem space of investigating computing systems in I-IoT as shown in Figure 10 . The framework consists of three dimensions, including computing tasks, physical systems, and computing requirements. Particularly, the computing resources are utilized for several computing tasks (i.e., storage, transmission and analysis). The FA and PA are typical examples in physical systems. The computing requirements are QoS-related metrics (latency, reliability, etc.) and others (security, etc.). With the proposed threedimensional framework, we can investigate existing research efforts by mapping them into the specific cubes shown in the figure.