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understand the complexity of Industry systems and processes. • Decentralized process modeling, and other modeling techniques into our study​. Adopting a .. supposed to control its production processes in many visions of Industry
Table of contents

• Introduction
• Industrial process control optimization and Industry
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For this, we studied the communication latency between the different IIoT layers in different IoT gateways. Industrial processes need most tasks to be conducted locally due to time delays and security constraints, and structured data needs to be communicated over the internet. Fog computing is a potential intermediate software that can be very useful for various industrial scenarios. It can reduce and refine high volume industrial data locally, before being sent to the cloud.

It can also provide local processing support with acceptable latency for actuators and robots in a manufacturing industry [ 2 ]. The lack of interoperability between devices in the Industrial Internet of things IIoT considerably increases the complexity and cost of IIoT implementation and integration. The search for seamless interoperability is further complicated by the long lifetime of typical industrial equipment, which require costly upgrades or replacements to work with the newest technologies [ 3 ].

One of the novelties of autonomous robots applied to industry 4. In this paper, the focus is on the construction industry since it is one of the sectors where traditionally less advanced technology has been applied and is therefore suitable for the use of the new Technology of Industry 4. Construction companies have mostly been using UAVs for real-time jobsite monitoring and to provide high-definition HD videos and images for identifying changes and solving or preventing many issues [ 5 ].

They are also used for inspection and maintenance tasks that are either inaccessible, dangerous, or costly from the ground [ 6 ]. Moreover, a small error or delay beyond the tolerated limit could result in a disaster for various applications, such as UAV and aircraft manufacture and monitoring.

The off-board base station gives them higher computational capacity and the ability to carry out more complex actions using high-level programming languages, or leveraging services from computer vision tools by acquiring, processing, analyzing and understanding digital images in real-time. Computing capabilities can be extended to the cloud, taking advantage of the services offered, and saving the cost and energy consumption of an embedded UAV system.

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There is a growing trend towards the three-layer IIot architecture with fog computing, with a convergence network of interconnected and distributed intelligent gateways. Fog computing is a distributed computing paradigm that empowers network devices at different hierarchical levels with various degrees of computational and storage capacity [ 8 ].

In this context, fog computing is not only considered for computation and storage but also as a way of integrating the different new systems capable of interconnecting urgent and complex processing tasks. The fog can be responsible for technical assistance between humans and machines, information transparency, interoperability, decentralized decision-making, information security, and data analysis. Its notable benefits minimize human error, reduce human health risks, improve operational efficiency, reduce costs, improve productivity, and maintain quality and customer satisfaction [ 2 ].

Here, we propose a UAV-based IIoT monitoring and control system integrated into a traditional industrial control architecture by harnessing the power of fog middleware and cloud computing.

Introduction

The main aim of the work was to present an innovative concept and an open three-layer architecture, including a UAV, to enhance quality and reduce waste by introducing visual supervision through cloud services as part of the three-layer IIoT architecture with fog computing and a control system. We also analyzed the fog computing layer and the IoT gateways to comply with the requirements of interoperability and time latency. We developed a theoretical model to mathematically represent the end-to-end latency in UAV-based Industry 4.

We provide a comparative study of a fog computing system through different platforms and analyze the impact of these platforms on the network performance. The study involved monitoring the materials carried on conveyor belts and controlling the production process. This operation was considered as cost-effective and time effective and reduced the concrete batch production time. A proposal for an IIoT-based UAV architecture for monitoring and improving a production process using cloud computing services for visual recognition.

The Industry 4. In , Germany adopted the idea to develop its economy in the context of an industrial revolution with new technologies compatible with old systems [ 10 ]. One of the proposed solutions is smarter IoT gateways [ 11 ], which are the bridges between the traditional network and sensor networks [ 12 ].

An IoT gateway is a physical device with software programs and protocols that act as intermediaries between sensors, controllers, intelligent devices, and the cloud. The IoT gateway provides the necessary connectivity, security, and manageability, while some of the existing devices cannot share data with the cloud [ 13 ]. Due to the incompatible information models for the data and services of the different protocols, interoperability between the different systems with different protocols is always difficult. Up to only a few years ago the communication systems for industrial automation aimed only at real-time performance suitable for industry and maintainability based on international standards [ 15 ].

To connect the different industrial equipment and systems, the same standards and safety levels are required. Open Platform Communications Unified Architecture OPC UA is a machine-to-machine M2M communications protocol developed to create inter-operable and reliable communications and is now generally accepted as standard in industrial plant communications [ 16 ].

OPC UA is an independent service-oriented architecture that integrates all the functionality of the individual OPC Classic specifications into one extensible framework [ 17 ]. Girbea, et al. OPC UA can allocate all manufacturing resources, including embedded systems, to specific areas and extensible computing nodes through the address space and a pre-defined model. It solves the problem of unified access to the information of different systems [ 19 ].

The authors of [ 22 ] designed and implemented a web-based real-time data monitoring system that uses MODBUS TCP communications in which all the data are displayed in a real-time chart in an Internet browser, which is refreshed at regular intervals using HTTP polling communications. The success of the IIoT initiative depends on communication protocols able to ensure effective, timely and ubiquitous aggregation [ 23 ].

Implementing an Industry 4. The cloud robotics architecture is based on two elements: the cloud platform and its associated equipment and the bottom facility. Bottom facilities usually encompass all kinds of mobile robots, unmanned aerial vehicles, machines, and other equipment [ 25 ]. The next generation of robots will include interconnected industrial robots [ 26 ], cobots [ 27 ] and autonomous land vehicles AGVs [ 28 ].

Cobots support human workers in various tasks, while robots can carry out specific tasks, such as looking for objects or transporting tools. UAVs and drones are among the emerging robot technologies that leverage the power of perception science and are now the preferred remote sensing system for gathering data over long distances in difficult-to-access environments [ 29 ].

Drone cameras can collect remotely sensed images from different areas safely and efficiently. UAVs can save time and money in different sectors, such as agriculture, public safety, inspection and maintenance, transportation and autonomous delivery systems.

Many industries use drones or unmanned aerial vehicles to increase sensing and manipulation capabilities, autonomy, efficiency, and reduce production costs. In the construction sector, drones play a significant role in industrial sites; they can fly over and monitor an area by acquiring photos and videos. They can be used to check a given installation or production areas, to transmit data, monitor construction processes, and detect anomalies. As mentioned in [ 4 ] many applications have already been implemented in the construction and the infrastructure fields.

UAVs are also used for the real-time inspection of power lines. In [ 31 ], the authors implemented drones to detect trees and buildings close to power lines. They can also be deployed to monitor oil, gas and water pipelines. Industrial SkyWorks [ 32 ] employs drones for building inspections and oil and gas inspections in North America using the powerful machine learning BlueVu algorithm to process the data collected. They provide asset inspection and data acquisition, advanced data processing with 2D and 3D images and detailed reports on the property inspected.

Crack assessment systems for concrete structures are constantly improving thanks to computer vision technologies and UAVs. UAVs combined with digital image processing have been applied to crack assessment as a cost-effective and time-effective solution, instead of visual observation [ 33 ]. Image processing has become a significant asset for UAVs systems and not only in industry. Capturing footage and videos generates a huge amount of data, for which cloud computing is vital. Image recognition technology has a great potential in various industries and has been improved by deep learning and machine learning image recognition systems TensorFlow, and MATLAB or image processing techniques such as computer algorithms for digital image processing.

In [ 34 ], Machine Learning Techniques were used to estimate Nitrogen nutrition levels in corn crops Zea mays. The work described in [ 35 ] introduced a real-time drone surveillance system to identify violent individuals in public areas by a ScatterNet hybrid deep learning SHDL network.

In [ 36 ], the images from a drone camera were processed by the bag-of-words algorithm to detect crops, soils and flooded areas, with MATLAB to program the feature extraction algorithm. The system implemented image processing algorithms using the open source computer vision library OpenCV.

The main goal was to resolve the energy constraint without any wire connections or human intervention.

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The authors in [ 38 ] propose to move computationally-demanding object recognition to a remote computing cloud, instead of implementing it on the drone itself, by means of a cloud-based approach that allows real-time performance with hundreds of object categories. Other cloud-based platforms, e. These solutions provide image analysis through a real connection with the main application. The aforementioned studies show the significant advantages in different sectors of cost-effective and time-effective UAVs integrated with big data technology and machine learning.

However, as far as we know, no studies have so far been published on the integration of UAVs into a complete industrial production system. Thus, here we propose an industrial real-time monitoring system with UAVs, fog computing and deep learning in the cloud Figure 1. The proposed IIoT-based UAVs collect photos from an industrial plant, while the cloud processing platform analyzes them and sends the results to a control system.

Industry is taking advantage of ever more complex and sophisticated systems. Systems not designed to communicate across production lines often require integration with pre-existing devices. The challenge of interoperability is thus one of the main concerns in designing intelligent human-to-machine and machine-to-machine cooperation. Our aim was to design a drone-based monitoring system able to interact in real-time with industrial sensors, PLCs, and the cloud automatically via an IoT gateway as middleware, and to transmit data between the different systems securely.

We validated our proposed architecture in an industrial concrete production plant in a case study to improve production and reduce costs. A UAV monitoring system was elaborated as an industrial control system to reduce inspection time and costs. An overview of the approach can be seen in Figure 1. The first layer consists of an industrial control system connected to a central collection point, which is the IoT gateway.

The second layer is the fog computing layer for computation, storage, and communications. The last layer is a cloud back-end with image processing techniques. The fog layer connects the industrial control layer to the UAV system, the UAV system to the cloud, and finally the cloud to the industrial control system.