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Monitoring and management engineering technologies, as well as crude oil production, have recently advanced significantly. Technology advancements aside, monitoring, administration, wait times, and emergency response remain quite poor, especially for the crude oil production cycle, where real-time management and monitoring are difficult. Several methodologies and technologies utilized for continuously monitoring and managing crude oil manufacturing are analyzed in light of the integrating of the Industrial Internet of Things (IIoT) and Edge Computing (EC). The ultimate objective is to monitor and manage the whole value chain process to increase production dependability and availability and generate better economic products and services. This study goes over crude oil production guidelines and unresolved research difficulties. The IIOT and EC integration are viewed as a possible approach for establishing domestic services and corporate procedures for increasing crude oil production. One hundred eighty-eight relevant studies published between 2017 and 2023 were indexed in the WOS (Web of Science), SCOPUS, OnePetro, and IEEE Xplore databases. A survey based on a literature review showed 50 studies (selected from those studies through the literature review stages) and their architectures and frameworks to automate crude production to better monitor alerts and production control. This survey aims to provide a comprehensive overview of multiple studies to highlight the deficiencies in recent research and serve as a reference for subsequent endeavors to develop solutions for a straightforward process monitoring system. Such a system would empower process operators to promptly and effortlessly detect any sources of abnormality within the process.

As a promising platform on the Internet of Things (IoT), the smart Internet of Vehicle (IoV) has emerged with the advent of the key connectivity to Industry 4.0, i.e. Fifth-Generation Mobile Communication (5G). However, problems with adequate battery life, powerful computing, and energy economy have hampered the development of this technology in light of the enormous increase in data traffic in 5G and 6G mobile communication networks. To address these limitations, this study proposes an Internet of Vehicles (IoV) system empowered by Edge Computing (EC), wherein intelligent vehicle nodes interact with an anchor node integrated with an EC server for data upload and download. Rather than solely focusing on enhancing the central cloud infrastructure, the integration of EC and IoT enables real-time and efficient services, thereby bolstering the storage and processing capabilities of underlying networks. By employing an offloading strategy within the Edge Computing-based Internet of Vehicles (EC-IoV) framework, users can allocate their workloads to suitable EC servers, leading to improved resource management and computational capabilities. However, challenges persist in evaluating the impact of uncertain user-EC server connectivity on offloading decision-making and mitigating potential declines in offloading efficiency.

Distributed denial of service (DDoS) is an awful cyber threat, becoming more prevalent with mature heterogeneous IoT (HetIoT) applications like intelligent agriculture, wearables, and self-driving cars. Developing intelligent intrusion detection systems (IDS) using deep learning (DL) techniques to protect HetIoT has sparked a lot of attention. Federated learning (FL) helps to train the IDS locally with local data while respecting data privacy. Edge computing (EC) enhances security by processing data closer to the edge network. Therefore, the research contributed by integrating EC, FL, and DL and the proposed Edge-FL-based IDS. The proposed Edge-FL-based IDS aims to enhance HetIoT security by safeguarding data privacy against DDoS attacks. The research developed a DL-based convolutional neural network (CNN) classifier and used the CICDDoS2019 dataset to evaluate the success rate of the proposed Edge-FL-based IDS against DDoS attacks. The research employed IID and non-IID data distributions with participant clients K =3, K =5, and K =7. The findings indicate that the proposed Edge-FL-based IDS outperforms centralized and other state-of-the-art FL models. The proposed Edge-FL-based IDS correctly detects and classifies DDoS attacks with the following accuracy: (a) 8-class IID: K =3 is 99.98%, K =5 is 99.97%, K =7 is 99.96%. (b) 8-class non-IID: K =3 is 99.97%, K =5 is 99.94%, K =7 is 99.90%. (c) 12-class IID: K =3 is 99.96%, K =5 is 99.96%, K =7 is 99.95%. (d)12-class non-IID: K =3 is 99.07%, K =5 is 96.44%, K =7 is 52.39%.

Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.

The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios.

Integrating Software-Defined Networking (SDN) and Edge Computing (EC) technologies becomes a promising approach to harness enormous data traffic in the Internet. Recent studies examined the Quality of Service (QoS) based routing paradigms for different data applications such as IoT, heterogeneous networks, and 5G network slicing in such integrated networks. In this paper, our study formulates the QoS-specific routing optimization as a minimum-cost resource allocation problem, while multi-dimensional factors of bandwidth cost, latency, and packet loss are jointly considered. Since we use the fittingness factor to help the problem solving, we propose a path selection scheme based on the measure of fittingness factor, briefly named as FitPath. The FitPath scheme can appropriately adjust network resources to maintain delivery time against various service requirements of user demands. Simulation results show that the FitPath scheme can perform better than the Open Shortest Path First (OSPF) and Greedy heuristic schemes in terms of the total of link costs, delivery latency, and packet loss rate under both of normal and heavy data traffic experiments. The effects by the FitPath scheme can be further enhanced with the top- $B$ % path selection policy in integrated network environments.

Dynamic service orchestration is becoming more and more necessary as IoT and edge computing technologies continue to advance due to the flexibility and diversity of services. With the surge in the number of edge devices and the increase in data volume of IoT scenarios, there are higher requirements for the transmission security of privacy information from each edge device and the processing efficiency of SFC orchestration. This paper proposes a kind of dynamic SFC orchestration security algorithm applicable to EC-IoT scenarios based on the federated learning framework, combined with a block coordinated descent approach and the quadratic penalty algorithm to achieve communication efficiency and data privacy protection. A deep reinforcement learning algorithm is used to simultaneously adapt the SFC orchestration method in order to dynamically observe environmental changes and decrease end-to-end delay. The experimental results show that compared with the existing dynamic SFC orchestration algorithms, the proposed algorithm can achieve better convergence and latency performance under the condition of privacy protection; the overall latency is reduced by about 33%, and the overall convergence speed is improved by about 9%, which not only achieves the security of data privacy protection of edge computing nodes, but also meets the requirements of dynamic SFC orchestration.

In view of the inherent defects of traditional network protocols, unreasonable video content distribution process, content scheduling and routing mechanism need to be optimized in the current edge computing (EC) network. A centralized control EC network architecture of named data networking (NDN) is proposed in this paper. EC nodes are deployed on the edge of the network to provide caching and computing capabilities, and distribute video streams. So that transmission delay is reduced, bandwidth cost is saved, and the quality of experience (QoE) is improved. Firstly, the communication mechanism of NDN is introduced to distribute content and improve the efficiency of content distribution. In the scheme, the content distribution mechanism of video stream is improved in a distributed way. Different bit rate versions of video clips are distributed and buffered on the distributed EC nodes, so as to avoid redundant cache and save storage resources. In addition, the user interest content model is constructed. That is, video is divided into different categories according to content and user content preference model. Finally, with the limited cache and computing resources of EC nodes, the software defined network (SDN) mechanism is introduced for centralized management and control. And it provides a solution for content scheduling problem under resource constraints. In the scheme, the content distribution mode is decided by the effective arrangement of network resources, and the user's request is processed and responded cooperatively to avoid redundant cache and transmission. Based on the selection of forwarding response mode, the distributed characteristics of EC nodes and the routing and forwarding mechanism of NDN are fully utilized to provide users with response. It is shown by the experimental results that the proposed scheme optimizes the load on the network side and the experience of the client to a certain extent.

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.