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The I/O layer is the first layer related to the production field controlled by sensors. The process of machines can be changed by actuation commands provided by the processing layer. The actuation commands must ensure the production synchronization of the whole production lines managing different production steps. In this layer, IoT devices are very important for the accuracy and reliability of the performed measurements. The data sampling is essential for a correct monitoring procedure. When the sensors control different production process steps, it is fundamental to configure and to synchronize all the sensors of the same production line. The IoT technologies are defined for the specific production process to monitor. For example, if the process is fast, it is important to select an image vision technology having a high frame rate, or sensors having a sampling time “following” the production velocity. The technologies for industrial image vision converting light into electrons are charge‐coupled device (CCD), complementary metal oxide semiconductor (CMOS), indium antimonide (InSb) infrared (IR) detectors, indium‐gallium‐arsenide (InGaAs), germanium (Ge), and mercury cadmium telluride (HgCdTe) sensors. Table 1.1 shows the working wavelengths of the IR technology.

Table 1.1 Spectral ranges of infrared technology.

Infrared technology	Spectral range (μm)	References
InSb	0.6–5	[14]
InGaAs	0.9–1.7	[14]
Ge	0.8–1.6	[15]
HgCdTe	1–9.5	[14]

Sensor networks are designed after an accurate analysis of the production processes, thus suggesting the correct configurations and connections of possible gateways, routers, and of device combinations. Sensor networks are implemented for point to point, star, extended star, bus, or mesh configuration. In Figure 1.1 are shown the different main network configurations. The design of the network is an important step for the realization of the correct network. The spatial allocation of the production machines and the workflow of the production define the best configuration. The network layout changes with the sensor system: the star or mesh network is typically adopted for sensors, besides the bus layout is suitable for production line connections and for the information system. By considering for example a photovoltaic camp with a high number of panels, it is preferable to realize a ring type fiber optic network linking all electrical string panels. The network also assumes a hybrid configuration, especially when a new network is added and linked to an old one. Table 1.2 lists the main advantages and disadvantages of the different network layouts.

Figure 1.1 Example of network configurations: (a) point to point connection; (b) bus line; (c) ring layout; (d) star connection; (e) tree layout; and (f) node meshing configuration.

Table 1.2 Advantages and disadvantages of network typologies.

Network type	Advantages	Disadvantages
Star	The star network manages the whole network by a single node behaving as a master node. Each node of the network can be added, removed, and reconfigured by ensuring the network operations. Network simplicity. Easy identification of errors	For a failure of the central node the whole network is out of order. Bandwidth limitation
Bus	Low cost and simple layout. Connection with a simple coaxial or RJ45 cable	For a failure of the bus the whole network is out of order. Additional nodes decrease network velocity. Single direction transmission mode (half duplex)
Ring	Bidirectional transmission mode for dual ring typology (full duplex)	Half duplex modality for basic ring configuration. Transmission security (if a node fails the network stops operating)
Tree	By adding to the tree network a star and a bus layout, it is possible to allow an easy addition of nodes and a network expansion	If the root node is out of order the whole network fails. Network performance decreases for complex hierarchical layouts
Mesh	Reliable and stable network type. Resistance to failure conditions also for complex layouts involving more interconnections	High time for network setting. High computational cost for complex interconnection layouts

The network typology must be compatible with the network information system of the industry. In this way, the hybrid solutions potentially ensure the best network performances and flexibility. Figure 1.2a shows an example of a hybrid extended star network, constructed by merging an extended star with a mesh network, by showing an example of network reconfiguration in cases of connection failures, where the automatic principle of node commutation is managed by an intelligent algorithm detecting and predicting system failures (example of direct interaction between processing layer and production machine layers). The cases of Figure 1.2b–e are related to a possible configuration of the data transmission of a part of the network of Figure 1.2a. This example highlights the importance of adding nodes to avoid the transmission problem. The solution to add nodes to the local network must be “weighted” with the decrease of performance due to the increased complexity of the new hybrid network. The prediction of possible failures of nodes, allows to change anticipatedly a linking configuration, thus avoiding data interruptions, and preserving production control. In the prediction calculation, sensors play an important role because they detect operation conditions of production lines, status machines and product tracking. In Table 1.3 and Table 1.4 are listed the main specifications of traceability sensors able to detect the product in each production stage, and the main characteristics of transmission protocols, respectively. Solutions for the actuation are the plug and play (P&P) solutions and programmable logic controller (PLC) hardware interfaces. For P&P systems the hardware and software components are downloaded and installed at or before run‐time. The supervisory control and data acquisition (SCADA) [30] systems are able to read production data and transmit the setpoints to the PLCs. SCADA systems typically are implemented to control system architectures by graphical user interfaces (GUIs), and behaving as a supervisor of peripheral devices such as PLC, and proportional integral derivative (PID) controllers interfacing process plant and production machinery. Typically, SCADA adopts visualization tools and synoptic graphics for real‐time data display.

Figure 1.2 Example of hybrid extended star network and failure system reconfiguration for a secure production monitoring: (a) hybrid network structure by extended star and mesh network; (b) normal configuration for data transmission to the manager central node (transmission from node 3 to node 1); (c) example of reconfiguration for an interrupted linking between node 1 (network coordinator) and node 2; (d, e) examples of reconfiguration for interrupted links between node 1 and node 2 and between node 1 and node 4 simultaneously.

Table 1.3 Main specifications of sensors used for traceability.

Sensor type	Main specifications	References
Barcode	Optical laser reading identifying only type of itemOnly read<20 of characters of data capacity	[16, 17]
QRcode	Optical laser reading identifying only type of itemOnly readUp to 7089 characters of data capacity	[17, 18]
RFID	Radio frequency detection system100–1000 characters of data capacityRead and writeStandards: 125–134 kHz (LF); 13.56 MHz (HF); 866–915 MHz (UHF); 2.45–5.8 GHz (microwave)Active tags (100 m reading distance, 64 byte–32 KB of memory capacity)Passive tags (1 m reading distance, 48 byte–2 KB of memory capacity)	[16, 17,19–21]
NFC	Distance of communication: few cmTechnology: RFID basedFrequency: 13.56 MHzCommunication: two way	[21]
iBeacon	Bluetooth Low‐Energy (low power consumption)1 m ± 70 m wireless range	[22, 23]

NFC, near field communication; RFID, radio frequency identification.

Table 1.4 Main specifications of sensor transmission protocols.

Standard	Main specifications	References
ZigBee	WirelessIEEE802.15.4 standardWPANFrequencies: 868 MHz (Eu); 915 MHz (US); 2.4 GHz30−100 m wireless rangeNetwork type: star, mesh, cluster tree, peer to peerCapacity: 250 Kbit s−1	[24–26]
WiFi	WirelessIEEE802.11 standardWireless local area networkFrequencies: 2.4–5.4 GHz1 km wireless rangeCommunication type: point to multipointCapacity: 54 Mbit s−1	[26]
Bluetooth	WirelessIEEE802.15.1 standardWPAN networkFrequencies: 2.45 GHz1–100 m wireless rangeCommunication type: point to multipointNetwork type: starController: system on chipData rate: 1 Mbit s−1Capacity: 723.1 Kbit s−1 (versions 1.1 and 1.2); 3 Mbit s−1 (version 2.0)Bluetooth V4.0	[21, 24]
RS‐232	WiredLAN/PAN networkCommunication type: point to pointNetwork type: busCapacity: 75 bit s−1–115.2 Kbit s−1.	[27]
USB	WiredLAN/PAN networkCommunication type: point to pointNetwork type: tree, busCapacity: 12 Mbit s−1 (version 1.1); 480 Mbit s−1 (version 2.0); 4800 Mbit s−1 (version 3.0)	[28]
Ethernet	WiredLAN networkCommunication type: point to pointNetwork type: star, busCapacity: 10 Mbit s−1–10 Gbit s−1	[29]
ZWAVE	Radio frequency technologySub‐GHz communications (900 MHz)Mesh networkNo coordinator nodeMaster/slave architectureData rates: 9.6/40/100 Kbit s−1	[24]
Fifth Generation (5G)	Cellular network standardHigher throughputLower latencyArtificial intelligence capabilitiesVideo real‐time processing	[24]

WPAN, wireless personal area network.

The information levels of a dynamic information system, allowing the upgrade from Industry 4.0 to Industry 5.0 production, is interconnected as in Figure 1.3, where the following six main layers are distinguished:

 Sensor and actuator layer.

 Agent, firmware and user interface layer.

 Gateway layer.

 IoT middleware.

 Processing layer.

 Application layer.

Figure 1.3 Layers of technologies related to an advanced technology.

Flexible technologies must act in these layers, and are fundamental to automatize all the production processes for:

 In‐line/off‐line production monitoring.

 The elimination of possible failure conditions.

 The decrease of production defects.

 The optimization of human resources and of their work.

 Business intelligence (BI) and strategic marketing.

 The optimization of the warehouse management.

 A dynamic production following the real‐time customer requests.

The main flexible technologies are integrated in robotic systems. Robots process information acquired by sensors placed inside and outside the production machines, and generating different outputs suitable for decision making, for the processing coordination, and for the system control.

The flexibility of the information system is mainly in the interconnectivity of all the layers shown in Figure 1.3, representing a standard architecture upgraded by AI and big data system working in the processing layer. Big data systems are characterized by the following features:

 Volume (dataset volumes larger than terabytes [1012 byte] and petabytes [1015 byte]).

 Velocity (velocity refers to the data generation speed).

 Variety (variety of sources with structured, semi structured and unstructured data).

 Veracity (quality of the data that is being analyzed, the non‐valuable data are classified as nose or wrong data).

Big data uses the not only SQL (NoSQL) technology. The NoSQL databases (DBs) do not use the relational model, are performed efficiently on clusters, and can be open source.

A primary important aspect concerning the production optimization is the production traceability, performed by sensors. Digital traceability is fundamental in Industry 4.0 scenarios. Automatic detection by gates installed on the production line at each production stage is able to control quality processes and production in general. Table 1.3 lists the main sensors used for product traceability.

Radio frequency identification (RFID) systems are constituted by a reader and by a TAG or transponder, enabling the electronic identification of the traced product. The active version is equipped with a lithium (Li) battery or is powered by an external source. The passive RFID is not equipped with a battery and is cheaper offering an infinite lifetime. Besides, the active TAGs are more useful when writing operations. Sensors displaced to control production transmit data using specific protocols. Table 1.4 lists some specifications of transmission protocols.