identified under the term self-X [206]. In addition to improving the overall performance of the systems, smart sensors allow for quicker response to modifications, repairs, and failures. They are also considered as an essential component in the prospect of predictive maintenance and could identify the service time of machines before they break down. This alleviates the problems of unplanned production outages and reduces maintenance downtime through better monitoring.
Altogether, the connection of smart devices and systems in Industry 4.0 environment organically pertains to every stage of the supply chain. This leads to decreased cost and more efficiencies in the value chain.
1.8.2 Criteria for Adoption of Smart Sensors for Industry 4.0
Currently, various advanced sensors are utilized in different industries such as logistics, agriculture, rail and traffic control, and shipping. The principal objective for adoption of smart sensors is to enhance systems quality, reliability, and precision [207]. Despite advancement of smart sensors, their implementation in Industry 4.0 is generally limited by noise and signal attenuation. There are two principal measures for adopting smart sensors in Industry 4.0:
Interoperation and interconnection: Smart sensors are of multi-vendor nature, and their interoperability is necessary, particularly for crucial sensors metadata such as timestamp, validity of data, sensor’s geo-location, and device status. Thus, it is essential to ensure their integrity and compatibility with current and emerging IIoT systems. New standards for smart sensors provide effective configurations, integrations, and improved calibration [208, 209]. Similarly, interconnections between multiple smart sensors and communication technologies hamper interoperation and lead to system complexity and deficiency. Ultimately, successful deployment should be contingent on legacy ecosystems, and strong implementation plans are required based on the business, industry, and circumstances. To achieve this, some advanced technologies provide solutions for nonsafety applications [210].
Security and trust: An important criterion for smarts sensors adoption in industrial applications is trust and security. The notion of trusting a sensor and its performance is important, particularly for control and safety applications. Therefore, both sensors and communication protocols that collect sensory data should be secure, trusted, accurate, calibrated, reliable, and timely.
There are trade-offs with respect to smart sensors selection such as complexity, ease of deployment, cost, and maintenance. To facilitate deploying smart sensors, the concept of sensing as a service is a possible solution where equipment, data capture, and management are leased or offered to assist in using smart sensors; however, full control over the sensor features will be compromised as a drawback [211].
1.8.3 Key Leverage for Smart Sensors in Supply Chain 4.0
Based on the supply chain operations reference (SCOR) [212], Supply Chain 4.0 has four stages: plan, source, make, and deliver. Implementing smart sensors is beneficial to all stages and can provide E2E insights for the company. Smart sensors enable real-time inventory management and identify performance metrics that improve inventory, supply planning, product design, and development. They also provide an efficient, transparent, and traceable flow of raw material from sources to customers and ensure accurate and consistent supply. Monitoring and predictive maintenance of machinery leverage smart sensors data and improve performance of fully connected production facility.
1.9 Future Trends in Wireless Communication for Industry 4.0
The digital transformation to Industry 4.0 and FoF is not easily implemented, as there are diverse use cases, connectivity requirements, and multiple levels of QoS that should be considered in designing and deploying each application. Therefore, Industry 4.0 faces critical challenges that suggest future research trends for 2022 and beyond. Some major topics in this domain are:
Development of new AI-enabled solutions: The integration of deep learning, data analysis, and artificial intelligence technologies, along with the industrial Internet, assists in offering smart monitoring and intelligent production and services.
Implementing Edge and Fog computing: Since cloud computing systems may suffer from capacity scarcity and experience high latency, Edge- and Fog-based computing are promising solutions for low-latency and time-critical applications. In this context, a novel mobile network architecture is presented in [213], where the radio access network (RAN) relies on Fog computing to address latency issue and leads to a more reliable system.
Cybersecurity and privacy: It is an important concern in IIoT scenarios as a heterogeneous connected environment and becomes more critical in post-Covid working trend due to worker expansion and remote working. Recent advancements in blockchain technology, wireless communication, and Edge computing offer trusted, distributed, and P2P network for failure prediction in IIoT that could improve security and intelligence of such systems [204].
Despite the fact that wireless communication is well established for some industrial use cases, heterogeneity of the connectivity landscape and its integration in the system pose critical issues in the industrial Internet. In this section, we identified some principal challenges of future wireless communication in Industry 4.0 and discuss them in the following sections.
1.9.1 Diverse Communication Requirements for Different Industrial Use Cases
As we discussed in Section 1.3, the requirements of wireless industrial Internet for various types of applications widely vary in terms of energy efficiency, deployment complexity, latency, and MAC protocols. Many wireless technologies, standards, and protocols are used in IIoT and smart manufacturing systems, and managing their coexistence in a system is still an open question. For instance, deterministic transmissions are highly important in control, process, and operation systems and should be guaranteed in coexistence with various wireless networking technologies. Additionally, issues arise due to the nature of wireless medium such as limited spectrum, shared bandwidth, reliable durability, and availability.
Altogether, E2E communication is highly challenging in industrial environment, and it highlights the necessity for management and optimization of wireless networks in Industry 4.0. The main objectives of network management for wireless industrial Internet are (1) real-time and dynamic wireless network optimization and management to offer flexible communication; (2) network resource allocation at different levels of system (equipment, production, operation and enterprise planning levels) to ensure required QoS; and (3) monitoring workflows to improve a network’s visibility and performance.
1.9.1.1 Possible Solutions
One approach to guaranteeing the required QoSs in compliance with service-level agreements is to deploy a unified middle-ware that integrates various wireless technologies tailored for individual applications. Emerging technologies such as NFV, SDN, and distributed Edge computing could be leveraged for a smart and uniform platform to enhance network management and visibility [214]. The middle-ware could also provide standard interface to integrate workflows, cellular technologies, and private networks [215]. A 5G cellular network is an example of standardized technology that copes with diverse requirements and QoS levels.
1.9.2 Challenges in Cellular and Mobile Technologies for Industrial Networking
A major wireless communication technology in the industrial Internet is cellular and mobile technologies such as 5G and B5G (Beyond fifth-generation), which are better suited to high-performance and fast motion applications in harsh environments. 5G offers E2E communication through public and private/dedicated connectivity in a highly flexible, reconfigurable, reliable, and power efficient manner. This makes it suitable for a wide range of distributed industrial use cases. Even though 5G significantly transforms wireless communication in terms of the technical requirements such as low latency, high bandwidth and data rate, and dynamically adapts with a proliferation of equipment, operations, and processes in IIoT environments, additional efforts are needed to encounter synergy between communication