A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> C. EDGE COMPUTING <s> The unyielding trend of increasing cyber threats has made cyber security paramount in protecting personal and private intellectual property. To provide a highly secured network environment, network threat detection systems must handle real-time big data from varied places in enterprise networks. In this paper, we introduce a streaming-based threat detection system that can rapidly analyse highly intensive network traffic data in real-time, utilising streaming-based clustering algorithms to detect abnormal network activities. The developed system integrates the high-performance data analysis capabilities of Flume, Spark and Hadoop into a cloud-computing environment to provide network monitoring and intrusion detection. Our performance evaluation validates that the developed system can cope with a significant volume of streaming data in a high detection accuracy and good system performance. We further extend our system for edge computing and discuss some key challenges, as well as some potential solutions, aiming to improve the scalability of our system. <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> C. EDGE COMPUTING <s> Industry 4.0 or industrial Internet of things (IIoT) has become one of the most talked-about industrial business concepts in recent years. Thus, to efficiently integrate Internet of things technology into industry, the collected and sensed data from IIoT need to be scheduled in real-time constraints, especially for big factories. To this end, we propose in this paper a hierarchical fog servers’ deployment at the network service layer across different tiers. Using probabilistic analysis models, we prove the efficiency of the proposed hierarchical fog computing compared with the flat architecture. In this paper, IIoT data and requests are divided into both high priority and low priority requests; the high priority requests are urgent/emergency demands that need to be scheduled rapidly. Therefore, we use two-priority queuing model in order to schedule and analyze IIoT data. Finally, we further introduce a workload assignment algorithm to offload peak loads over higher tiers of the fog hierarchy. Using realistic industrial data from Bosch group, the benefits of the proposed architecture compared to the conventional flat design are proved using various performance metrics and through extensive simulations. <s> BIB002
In contrast, edge computing leverages computing resources from network edge devices. Compared to cloud computing, edge computing can provide computing services with much better latency performance, as they are usually located close to end-devices  , BIB001 . Concerning general edge computing, ur Rehman et al.  proposed the concentric computing model (CCM). The CCM deploys the multi-granular interoperable computing devices and systems in the edge layer of a multi-layer architecture. The design of the CCM considers the performance of devices and systems, locations of sensors, devices, servers and data centers, as well as the performance requirements of big data analysis applications, such as bandwidth utilization, latency, resource efficiency, etc. In addition, the challenges of real-time data analytics and streaming, data integration and extraction, and security and privacy were discussed. This work can be mapped to cube <Analysis, PA/FA, QoS> shown in Figure 10 . Furthermore, Chekired et al. BIB002 proposed a multi-tier edge computing architecture to reduce computation and communication latency. In particular, data was divided into two categories (i.e., high priority and low priority) to improve the efficiency of the data computation and transmission. Experiments were conducted to evaluate the effectiveness of two-tier, three-tier, and flat edge computing architectures. The results demonstrate improved performance in their multi-tier edge computing architecture with respect to latency, computational capacity, and other metrics with different workloads. This work can be mapped to the <Transmission, FA, QoS> space shown in Figure 10 . 