Industrial Internet of Things 4.0

In the era of mechanical digitalization, organizations are progressively putting resources into apparatuses and arrangements that permit their procedures, machines, workers, and even the products themselves to be incorporated into a solitary coordinated system for information assortment, information examination, the assessment of organization advancement, and execution improvement. This chapter presents a reference guide and review for propelling an Industry 4.0 venture from plan to execution, according to base on the economic and scientific policy of European parliament, applying increasingly effective creation forms, and accomplishing better profitability and economies of scale may likewise bring about expanded financial manageability. This chapter present the contextual analysis of a few Industry 4.0 applications. Authors give suggestions coordinating the progression of Industry 4.0. This section briefly portrays the advancement of IIoT 4.0. The change of ubiquitous computing through the internet of things has numerous difficulties related with it.

Internet of Things and Robotic Applications in the Industrial Automation Process

The recent industrial scenarios project its advancements and developments with the intervention of integrated technologies including internet of things (IoT), robotics, and artificial intelligence (AI) technologies. Industrial 4.0 revolutions have broken the barriers of all restricted industrial boundaries with the act of those interdisciplinary concepts and have taken a keen part in industrial development. Incorporation of these advancements considerably helps in improving product efficiency and in reducing the production cost. Based on categories of production, industrial automation processes may vary. In this regard, robots are playing a vital role to automate the production process at various levels of industrial operations. The combination of IoT, robotics, and AI technologies enhances the industrial productivity towards getting the success rate. This chapter focuses on how robotic technology with IoT and AI methods enhances the limitations of various industrial applications.

Towards the Protection and Security in Fog Computing for Industrial Internet of Things

Internet of things (IoT) has given a promising chance to construct amazing industrial frameworks and applications by utilizing wireless and sensor devices. To support IIoT benefits efficiently, fog computing is typically considered as one of the potential solutions. Be that as it may, IIoT services still experience issues such as high-latency and unreliable connections between cloud and terminals of IIoT. In addition to this, numerous security and privacy issues are raised and affect the users of the distributed computing environment. With an end goal to understand the improvement of IoT in industries, this chapter presents the current research of IoT along with the key enabling technologies. Further, the architecture and features of fog computing towards the fog-assisted IoT applications are presented. In addition to this, security and protection threats along with safety measures towards the IIoT applications are discussed.

The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

Digitalization and Automation in Agriculture Industry

The industrial production has experienced a technological revolution in the recent past decades. The technological revolution influenced the agriculture industry too. The important areas in the change are not limited to innovation in farming, novel production of agriculture-based tools and equipment, transportation and consumption of food across the globe, marketing the agriculture products, and digitalization. Digitalization is the involvement of digital technology in the existing field for easing the mechanism of handling, processing, recording the data. Digitalization enables sustainable farming. It is required desperately to develop this technology because there is a substantial reduction of clean water and depletion of aquifers effects the cultivation. With the technology, the quantity and quality of the food has to be managed to feed the global population. The familiar digitization technology that makes the agri-industrial sector to experience growth are artificial intelligence, machine learning, sensor networks, internet of things, robotics, cloud data.

Blockchain-Enabled Decentralized Reliable Smart Industrial Internet of Things (BCIIoT)

The development of wireless communication in the information technological era, collecting data, and transfering it from unmanned systems or devices could be monitored by any application while it is online. Direct and aliveness of countless wireless devices in a cluster of the medium could legitimate unwanted users to interrupt easily in an information flow. It would lead to data loss and security breach. Many traditional algorithms are effectively contributed to the support of cryptography-based encryption to ensure the user's data security. IoT devices with limited transmission power constraints have to communicate with the base station, and the data collected from the zones would need optimal transmission power. There is a need for a machine learning-based algorithm or optimization algorithm to maximize data transfer in a secure and safe transmission.

Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain, and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases that may be queried during planning and execution. In this chapter, the authors propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, they model the knowledge of world models, robotic capabilities, and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. They demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.

Challenges to Industrial Internet of Things (IIoT) Adoption

The industrial internet of things (IIoT) has made its development within a short span of time. Initially it was considered as a novel idea and currently it is a major driver in industry applications. It has created productivity and efficiency for industries worldwide. This innovative technology can become a practical reality if engineers overcome a variety of challenges. They are connectivity, cost, data integration, trust, privacy, device management, security, interoperability, collaboration, and integration. In this chapter, several facts behind the above-mentioned challenges are being explored and addressed.

Realizing a Ultra-Low Latency M-CORD Model for Real-Time Traffic Settings in Smart Cities

At present, the need for an ultra-high speed and efficient communication through mobile and wireless devices is gaining significant popularity. The users are expecting their network to offer real-time streaming without much latency. In turn, this will result in a considerable rise in network bandwidth utilization. The live streaming has to reach the end users mobile devices after traveling through the base station nodes, core network, routers, switches, and other equipment. Further, this will lead to a scenario of content latency and thereby causing the rejection of the mobile devices users' request due to congestion of the network and mobile service providers' core network witnessing an extreme load. In order to overcome such problems in the contemporary 5G mobile networks, an architectural framework is essential, which offers instantaneous, ultra-low latency, high-bandwidth access to applications that are available at the network edge and also making the task processing in close proximity with the mobile device user.

Enhancing In-Service Tank Maintenance Through Industrial Internet of Things

In the area of tank inspections across the industry, robots were introduced to replace human inspectors in selected operations. The technological gap in adoption of similar technologies by Zimbabwe's bulk fuel storage tanks operators motivated this research. The industry's current NDT practices were investigated, costs and inconveniences were identified, and improvements were explored. Operators of bulk fuel facilities and companies providing tank inspection services were engaged to establish the reasons for the gaps in technological assimilation. Emerging global technologies that enable in-service inspections were identified and their applicability to Zimbabwe's bulk fuel facilities was investigated. A combination of crawler based ultrasonic thickness tests for tank shells, and acoustic emission in-service tank bottom testing was observed to be the most convenient and relevant in-service tank inspection method for Zimbabwe's bulk fuel storage tanks industry. Internet-based remote connectivity and control was considered for data compilation, analysis, storage, and reporting.