The first industrial revolution, i.e. Industry 1.0, started in the eighteenth century. In Industry 1.0, manual labour-based industry towards the use of steam engines, etc., to increase the productivity was practised. This was also called mechanization of industries. Industry 2.0 started after a century with the utilization of electricity and assembly line productions. Further, Industry 3.0 was introduced with the advent of the memory-programmable computers and electronics control and came into mainframe in the twentieth century to boost the production process and product standardization. In recent times, automation has become the game-changing factor for manufacturing and process industries. The human interaction with the machines is reduced in manufacturing processes by the inclusion of digital process control and automation . The fourth industrial revolution, Industry 4.0, was initiated by the advanced and latest technological innovations which brought the cyber physical systems together, i.e. smart machines capable of exchanging real-time information over the Industrial Internet of Things (IIoT) for decision-making process.
The Industry 4.0 is known as the fourth industrial revolution, which represents a new industrial infrastructure based on cyber physical systems having embedded software connected through IoT to the physical word. Thus, this framework facilitates the easy and faster communication in the production process by creating a real-time optimization, modern control systems and enabling predictive maintenance by introducing machine learning (ML) and artificial intelligence (AI) component .
The improvements in cyber computing facilities are the main factor behind the popularity and reliability of Industry 4.0. These advanced technologies are responsible for narrowing the gap between virtual and real world by creating an interface between ICT and actual operations. The basic structure of Industry 4.0 is shown in Fig., and timeline of industrial revolution is shown in Fig.
The key components of Industry 4.0 are the industrial internet of things, which includes different types of sensors for controlling and monitoring the process parameters. These sensors relate to the process control equipment either remotely or physically. The role of smart sensors in Industry 4.0 is summarized in Fig. Smart sensors and actuators are the first levels of process automation. Basically, automation structure consists of different stages that has been classified as:
Stage 1—This stage consists of sensors and actuators, which collects raw data from manufacturing process and initiates the control by analysing that data.
Stage 2—This stage acts as an interface between process parameters and control parameters by means of various input/output modules.
Stage 3—This stage is known as controlling stage that consists of IT infrastructure, responsible for controlling the production processes.
Stage 4—The function of this stage is monitoring the whole manufacturing process as well as it consists of process management system.
Stage 5—This stage serves as interface consisting of detailed operational planning, quality specification management and data collection over the cloud.
In Industry 4.0, all the machines in the process plant are integrated with sensors having an IP address that allows them to communicate with other IoT-enabled devices. IoT-enabled devices make it possible for exchanging a large amount of data for a short span, to optimize and evaluate the performance of the whole system. Moreover, the future prediction, warnings, status updates, etc., about the behaviour of the machines are easily obtained from the analysis of collected data through the sensors. The measurement data collected through the sensors, process control algorithms, mathematical analysis with cyber secure infrastructure is the basis of digital transformation, which makes the quality infrastructure stronger with conformity assessment market surveillance and verification system. With the advancements in technologies and development of high-speed computerized software and hardware frameworks, the sensor technology has outgrown and has entered in a hi-tech stage, resulting in precision manufacturing technology with an objective of enhancing the operation monitoring capabilities and accuracy with increasing the efficiency of the process by creating a virtual link between the machines and end users. In the production system, each sensor collects some certain parameters and then the system uses signal processing algorithms and intelligent control techniques to relate all the measurement results in a comprehensive set. These sensors are directly connected to the measurement system or they collect and transfer the data through wireless signal.
The smart sensors, which are being used in Industry 4.0 for monitoring, measurement and control prospective, need precise calibration methods and standards for ensuring traceability to the national standards and accomplishing the highest levels of accuracy and precision and conformance to relevant national and international standards. Moreover, wireless or remote calibration is preferred for these smart sensors. The past standards which are being adopted for the calibration may not be completely suitable for smart sensor. Thus, the development of advanced calibration standards and methods integrated with IoT-embedded systems and machine readability for Industry 4.0 is the necessity for making the measurements highly accurate, precise and reliable. The manufacturing process has been revolutionized by fourth industrial revolution enabling self-sufficient manufacturing process by using machines and sensors, which can establish a communication link through digital connectivity. A smart factory is characterized with the help of (i) sensors which are having the capability to self-learn on the basis of environmental information and they can take part in the decision-making process according to the change in process variables, (ii) co-ordination between different devices through flexible interconnection, (iii) artificial intelligence (AI) allow smart factories to have an advance level of interconnection among various processes. Moreover, AI with human capabilities allows industries to take the decisions based on the analysis of data and (iv) the most important component of Industry 4.0, which enables human–machine interface by using signal processing techniques and simulating different conditions based on sensor data.
The design and fabrication of smart sensors have thus emerged as a leading research area for researchers in recent times. There is need of more and more accurate and precise measurements that lead to the reliable and robust design of smart sensors. Presently, sensors are being used to record and analyse the data from the most hostile and challenging environment like biomedical measurements, space applications, meteorology applications, physical and chemical metrology, electrical and electronic measurements. Sensors facilitate the condition monitoring of the machines, devices or structures wherein they are integrated. In spite of integrated sensor networks, wireless sensor network has long-term potential and importance, responsible for enhanced throughput based on the collected data. Wireless sensor networks face many conceptual and optimization problem like tracking, deployment and reliability-related issues. Wireless sensors are generally deployed in the harsh environment. Therefore, there may be faulty or unreliable communication with these sensors. The probability of signal delay and loss is common in wireless signal network. The very basic problem with the wireless sensors is the coverage-related issue, which affects the quality of service of the sensor. The major problems that are associated with the wireless sensor network are (i) selection of hardware and operating infrastructure, (ii) calibration, deployment and programming model for the sensing network and (iii) synchronization. Thus, it is imperative to counter these limitations associated with the wireless sensors for developing a reliable, economical and usable network that can be used in Industry 4.0 without any hesitation.
Wireless sensor and communications are the prerequisites for Industry 4.0. However, there are many challenges faced in order to provide a secure communication, which is not prone to attacks. One of the attacks is denial distribution of services. An innovative method to detect attacks on software-defined wireless sensor network is explained in reference. Wormhole is another threat to wireless sensors where an attacker introduces dangerous nodes and uses false routes established by the node. Hence, the route from the sensor is set up and the wormhole is detected using a MAC centralized routing protocol (MCRP). The blockchain methodology suggested in reference is used to keep the communication links in Industry 4.0 secure. Industry 4.0 is required to have reliable, low latency and energy-efficient communication to allow machine-to-machine communication . 5G provides the communication support for Industry 4.0 and allows the prolonged battery life. In 5G, there is an issue of resource allocation for the network user, which is the wireless sensor. Hence, network slicing is required to distribute the network to the N sensors. In an industry, there may be more than one sensor using the same network to send information. This increases the traffic in the network, which also causes the loss of data. These lost data must be retransmitted efficiently. The signal from the sensors in a distributed system in Industry may be mixed since the information is passed through air. So, a method of using dense convolution and federal learning is efficient to separate the signal.
Even though Industry 4.0 has many benefits, one of the requirements for digitalization is sharing of data but most of the companies are reluctant to share data as they are afraid of fall behind their competitors or being exploited by others. The digitalization possesses risks to the sensitive information, and privacy is being violated as the information is transparent. It infuses fear and lack of trust to adopt digital technologies by industries. In other words, the digital world should provide transparency of data with adequate security. The cloud services face many threats so that personnel with adequate knowledge of how to deal with this threat is necessary to use these services. Other challenges for the Industry 4.0 are considered to be lack of trained employees, lack of competencies, lack of financial resources, lack of standards and processes and resistance from the employees. These aspects have to be resolved step by step for synergising the complete process and establishment of a digital quality infrastructure in Indian perspectives. The trained manpower and experts from various fields have to play a key role in creating awareness and inculcating knowledge about various technical and legal considerations for the establishment of a foolproof system.
