The fourth industrial revolution revolutionized the Internet of Things (IoT). This age of the industrial revolution is also called the connected age [1]. The industrial revolution started in 1780 with the introduction of the mechanization age. In 1870, the second revolution, named the electrification age, was introduced. As a result of the advancement in technology, Industry 3.0, also known as the automated age, was adopted in 1970. However, with the introduction of IoT, Industry 4.0, also called the connected age, was fully utilized. Because of increased interconnection and smart automation, Industry 4.0 envisions a fast change in technology, industries, and social patterns and processes [2]. Industry 4.0, in essence, is the trend toward automation and data sharing in manufacturing technologies and processes such as cognitive computing [3], cyber–physical systems (CPSs) [4], the industrial Internet of Things (IIoT) [5], cloud computing [6], indoor factories [7], IoT, and artificial intelligence (AI). Figure 1 shows the evolution of the industrial revolution.

Industry 4.0 uses technologies such as DT and FL. DT is the digital processing and evaluation of an industry, smart city, or network. Intelligent manufacturing is composed of six layers; the zero layer is called the manufactory area; the first layer is known as the manufactory building; the second layer is infrastructure; the third layer is operations the fourth layer is the smart manufactory; and the fifth layer is DT [8]. All these layers are shown in Figure 2.

Across industries, simulations are used to test goods, systems, processes, and concepts. Simulations are generally digital models created with computer-aided design software tools and are frequently utilized during the design phase. These models can be constructed in 2D or 3D to depict different aspects of a process or product, but they can also be created with mathematical principles rather than computer-based representations. To analyze results, the simulation introduces and tests many factors in the digital world or interface.

A virtual representation that is constructed to accurately mirror an existing physical thing is referred to as a DT. The physical thing is outfitted with sensors that generate data regarding many elements of the device’s performance, such as on a wind turbine. This information is subsequently sent to a processing system and applied to the digital model. This DT may then be used to run simulations, analyze current performance, and create prospective enhancements that can be transferred back to the real asset. A DT for non-physical processes and systems may also be developed, replicating the underlying process or system, and allowing simulations to be undertaken using real-time data.

DT data is often obtained via Internet of Things (IoT)-connected devices, enabling the gathering of high-level information that can subsequently be merged into the virtual model.

A DT is essentially a virtual environment in which ideas may be explored with little constraints. With an IoT platform, the model transforms into an integrated, closed-loop twin that can be utilized to inform and drive company strategy.

Although both simulations and DT employ digital models to imitate items and processes, they have some fundamental distinctions. The most noticeable is that a DT generates a virtual environment capable of studying several simulations, supported by real-time data and a two-way communication flow between the twin and the sensors that gather this data. This improves the accuracy of predictive analytical models, allowing for better management and monitoring of products, regulations, and processes. A comparison of simulations and DT is presented in Table 1.

FL is defined as collaborative ML without centralized training data. In FL, the shared prediction model learns collaboratively and keeps all training data on the local devices, without storing all the information on a single server. FL provides several advantages, such as smarter models, low latency, less power consumption, and privacy preservation. FL is a new technology that enables phones to learn from other devices while keeping data private and secure. It works as follows. First, the phone downloads a generic ML model. Then, the phone personalizes and improves its model, and computes a summary of the changes. Thousands of summaries are anonymously combined when phones are plugged in at night. This provides a global improvement to the model, enabling it to work better for all users. Thus, users benefit from having a smarter phone, and their data remain in users’ hands [8]. Figure 3 demonstrates FL.

The industrial Internet of Things (IIoT), Internet of Vehicles (IoV), and Internet of Drones (IoD) are three important technologies of next generation networks (NGNs). IIoT is referred to as the industry of the future. IIoT refers to interconnected instruments, devices, and sensors networked with industrial applications on computers, such as for production and energy management. This connectivity enables data gathering, sharing, and analysis, possibly promoting productivity and efficiency increases and other economic benefits [9]. The IIoT is a development of a distributed control system (DCS) [10] that enables more automation by refining and optimizing process controls utilizing cloud computing.

IoV is a network of vehicles and allows the utilization of data generated by linked automobiles and Vehicular Ad Hoc Networks (VANETs). IoV is a decentralized network of vehicles outfitted with sensors, software, and technologies linking and exchanging data over the Internet following agreed-upon standards ^[11][12][11,12]. IoV emerged from Vehicular Ad Hoc Networks [13] (“VANET,” a type of mobile ad hoc network used for communication between automobiles and roadside equipment), and is predicted to grow into an “Internet of autonomous vehicles” in the future. The IoV is predicted to be one of the facilitators of autonomous, connected, shared, and electric (ACES) Future Mobility [14]. Figure 4 shows an illustration of the IoV.

The IoD is defined as an infrastructure that aims to offer control and access to drones and users over the Internet. Drones are quickly becoming commonplace commodities, allowing each user to fly many missions in regulated airspace. The IoD is a layered network control architecture developed primarily to coordinate the access of unmanned aerial vehicles (UAVs) to regulated airspace and deliver navigation services between nodes [15]. The IoD offers general services for various drone applications, including package delivery, traffic monitoring, and search and rescue ^[16][17][18][16,17,18]. Figure 5 illustrates the IoD.