Industrial IoT

Industrial IoT (IIoT) uses a network of connected objects or devices that relate to each other and the data they generate. This relates to the consumer use of Internet of Things (IoT), which includes smartphones, cars, refrigerators, thermostats, and mirrors. Industrial IoT, also referred to as Industry 4.0, uses similar technology and data combined with manufacturing and other industrial processing, often with the goal of automation, efficiency, and productivity. As well, IIoT can be used to develop new applications in industries.

IIoT has been applied, for example, in:

• factory equipment, machines, and manufacturing devices;
• health monitoring devices in healthcare;
• sensors and supervisory control and data acquisition systems in oil and gas production; and
• telemetry data from autonomous vehicles.

IIoT hardware includes a wide range of devices, such as those for routing, bridging, or sensing, which manage tasks and functions of system activation, security, action specifications, communication, and detection of support-specific goals and actions. These can include low-power boards and single board processors; data acquisition modules working to acquire physical things monitored by the hardware; and the hardware needed to process the data either in the IIoT device or once communicated, in a main or cloud-based platform.

Connectivity devices for IoT include any hardware that works to connect devices at any point in a network or ecosystem. These can include gateways, routers, and platforms. The hardware used will depend on the network type; solutions used; and the power consumption, range, and bandwidth consumption needs of a network. Connectivity options include wireless technologies, such as cellular, WiFi, bluetooth, satellite, and ethernet.

Sensors used in IIoT offer monitoring capabilities dependent on the desired use case, including using predictive maintenance, supervising facility conditions, and improving the safety of work environments. These sensors include smoke sensors, proximity sensors, infrared sensors, piezo sensors, temperature sensors, optical sensors, and image sensorsThese work to generate data on: temperature and humidity, dry contact closure, temperature of thermocouples, current information, cycle counters, pressures, vibrations, air quality, activity, and water activity. And these sensors can be used for monitoring environments, machinery, and equipment.

Industrial IoT software, also known as IIoT platforms, works to provide assistance with configuring, managing, and monitoring IoT devices and networks in IIoT scenarios. This can allow operators to optimize resource usage, improve product quality, and automate tasks, while ingesting data from the entire platform.

With the increase in connected devices, machine to machine (M2M) communications, and overall IIoT implementations, there is an increase in need for standardized protocols, especially as disparate devices will use disparate protocols. In this case, an application layer can offer a single protocol that collates the several possible standards in operation. The application layer is part of the structure of communication protocols for IIoT. Most models have three, four, five, or seven abstract layers, and in all models the application layer is the highest abstraction layer between devices that communicate with each other on a network. This is the highest level of abstraction in the system, and the layer with which users and system software interact.

There is no single consensus on IIoT architecture. The most basic architectures are three- and five-layer architectures, with the basic layers being the application layer, perception layer, and network layer.

Typical architecture for (A) three-layer and (b) five-layer architectures.

Then there are cloud- and fog-based architectures. In both of these architectures, the data processing is done by cloud computers, with the cloud at the center of the architecture and applications above it while the network of connected devices is below it. In fog computing, sensors and network gateways do a part of the data processing and analytic; the layers include monitoring, preprocessing, storage, and security between the physical and transport layers. Often fog and edge computing are used interchangeably, but a fog computing network offers a more generic version of edge computing, with the latter being more focused on having the computing and preprocessing of data done on the connected device.

Fog architecture of an IoT gateway.

An emerging paradigm in application layers is called Social IoT or SIoT. This considers the relationship between objects as a social relationship similar to those formed between people. This network sees IoT devices as navigable, allowing a user to start at one device and travel through to other connected devices and services on the IoT network. All the devices need a level of trustworthiness between devices to communicate. And often these types of models are similar to studying human social networks. This type of network is intended to allow the devices and services to develop relationships amongst themselves and cooperate with each other to achieve a task.

Edge software platforms for IIoT perform infrastructure tasks. These tasks include device management, application development, enablement, execution, hardware or software virtualization, and container orchestration. This software solution works to offer internal improvements, such as increased uptime, production flexibility, inclusion in connected end-products, and incremental service innovation. Edge platforms can also correspond to the breadth of served applications, such that implementations with legacy supplier competencies, geographic and industry presence, and ecosystem participation can be combined to extend the capabilities of an existing network.

Security remains an emerging software deployment in IIoT scenarios, especially as the type of security software depends on the network connectivity parameter used, such that security differs over internet-connected devices versus that required for Bluetooth-connected devices. Moreover, as IIoT implementation becomes more complicated, there is a requirement for multiple security controls implemented in the network to protect all devices and the data communications. With the different connectivity standards, different connectivity protocols, and the general lack of security on the devices themselves, the use of IIoT performing critical functions means the need for security is increasing.

IIoT implementations can use artificial intelligence to enhance big data analytics to better ingest and use different types of data, including raw and unstructured data, metadata, and transformed or value-added data. Artificial intelligence is used in managing these types of data in forms of identifying, categorizing, and decision making. And coupled with analytics, AI provides the ability to make raw data meaningful and useful for decision making purposes.

Industrial IoT networking is different than networking for consumers or enterprises, and the requirements from applications on the supporting networks are diverse and can span many different industry sectors, including utilities, transportation, manufacturing, and healthcare. The necessary equipment per deployment scenario can also vary, especially as the IoT operational factors increase or complicate. This also means the networking and related infrastructure and technologies are continuously evolving. And as new technologies are introduced, network capabilities grow to enable industrial assets, such as machines, sites, and environments.

The infrastructure for implementing a network for IIoT tends to start with the connectivity technology, such as 4G LTE or 5G or WiFi. The type of technology chosen depends also on the network requirements made by the different devices on a network, and the network can use multiple connection types if necessary. However, more IIoT systems are moving towards private cellular networks, as they tend to offer additional connectivity capabilities for IIoT applications.

Furthermore, more data-centric applications of IIoT require greater bandwidth in the network, which will, in turn, dictate the type of network used, especially with the network capabilities promised by 5G. In most applications, there is a need for a flexible and scalable network infrastructure for efficient, reliable, and secure data feeds. The protocols and topologies need to consider different factors, including application needs, coverage requirements, device type and location, power consumption, and budget.

As IIoT applications continue to expand, new strategies and technologies to deal with the increased demands on networks, security requirements, and application necessities have led to the development of stronger network infrastructure, faster data analysis, and prediction mechanisms. Access technologies will increase in necessity, especially as spectrum scarcity increases with the uptick in the number of active IoT devices on networks. Artificial intelligence solutions also promise to enable dynamic and adaptive technologies to provide automated access and spectrum solutions able to respond to network and data demands.

One important technology for the development of networks is the deployment of 5G mobile networks. These networks promise increased network coverage, higher data rates and capacity, and better system and spectral efficiency. 5G can also better support machine-to-machine communications while offering lower battery consumption, lower cost, and lower latency than 4G networks. And as the integration of 5G networks in IoT ecosystems continues, the technology is capable of accommodating existing cellular connectivity standards, such as LTE and 3G, to make upgrades to the new network technologies more cost-effective for corporations. This is especially important as 4G LTE networks are estimated to remain the dominant network technology until at least 2026.

The introduction of 5G networks also promises ubiquitous deployment of IoT technology, including carrier aggregation, multi-input multiple-output (MIMO), massive-MIMO, coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV), and cognitive radios. Many of these possible use cases are likely going to be driven by advances in artificial intelligence, machine learning, and deep learning when combined with the bandwidth and network capabilities of 5G.

The verticals of IIoT continue to expand as more uses for smart devices or connected devices are explored. This has included changing the ways we are able to communicate, shop for products, manage work responsibilities, navigate and experience driving, live in and manage buildings, track transportation and supply chain logistics, grow food, take care of our health, and manufacture goods.

Beyond manufacturing of automobiles, the implementations for IIoT in the automotive industry are everywhere. This can include in-fleet management, connected cars, automotive maintenance systems, autonomous vehicles, and in-vehicle infotainment and telematics.

For manufacturers, IIoT can offer a way to digitally transform the manufacturing process. Using this can use a network of sensors to collect production data and uses cloud software to turn the data into an understanding about the efficiency of manufacturing options. This can reduce the cost in manufacturing through optimized asset and inventory management, reduce machine downtime, create agile operations, and reduce energy use. Shorter time to market occurs, through faster and more efficient manufacturing and supply chain operations. IIoT offers the possibility for a mass customization process into the variety of produced SKUs, which causes inventory to go up and become more diverse. And IIoT could offer better safety for workers and create a safer workplace, when paired with wearable devices that can monitor workers' health and risk. IIoT in manufacturing also offers tighter quality management as IIoT devices can detect mechanical anomalies on the production line with computer vision before they can cause a greater impact on the final products. These systems can also be integrated with existing machines to collect and analyze performance data to improve the overall efficiency of a product line and to locate where slow-ups and inefficiencies occur.

For the management of buildings, IIoT offers a chance to develop building automation and management systems, offering simplified energy management and management of core building functions for heating, air conditioning, and ventilation equipment. As well, IIoT can offer insights into diverse building functions, including equipment reconfiguration, activation or shutdown, maintenance scheduling, or alert triggers. This includes using IIoT technologies to offer greater utility savings and reduce costs and reduce a building's environmental footprint, through understanding overheating and underheating across a property. This can also offer on-demand micro-zoned equipment to achieve higher energy efficiency. And, through occupancy data, building managers can find trends in HVAC and lighting to optimize equipment schedule and reduce energy use or develop consumption data to identify bottlenecks or counteractive measures.

Sensors in buildings can offer building owners unparalleled visibility into their property operations, including facility health, equipment condition, waste management, security, and fire safety. Water-leak detectors can notify of early-stage pipe failures to enable immediate valve shut-down to prevent water damage. And sensors can be used for input on inclination, vibration, crack formation, and humidity exposure; when paired with advanced analytics, data from scanners can offer insight into a building's structural integrity. For tenants or occupants, IIoT can increase occupancy comfort and offer greater comfort and wellbeing for occupants. And building managers can assess traffic and usage of different building areas to accordingly prioritize cleaning activities and a smart parking system to contribute to a positive tenant experience.

IIoT can also help building managers detect inefficiencies, improve leasing decisions, optimize office layouts for emerging workforce trends, and offer floor traffic data to help users enhance offerings during peak hours.

For fleet and logistics uses, IIoT can offer fleet management solutions, transport logistics solutions, smart parking, railways, and smart toll collection. IIoT can offer a lot of new real-time access to new data streams to reduce pollution, optimize the mobility of people and goods, and develop growth in the industry.

The use of IIoT in agriculture has been used for analytics, greater production capabilities, and pushing digital agriculture. Smart agriculture, as it is often called, is seeing wider adoption amongst farmers and the lower costs of sensors and drones have seen wider adoption in agricultural scenarios. These techniques can increase the efficiency of the day-to-day work on the farm. For example, sensors in fields allow farmers to obtain detailed maps of the topography and resources in the areas, as well as variables such as acidity and temperature of the soil. Sensors can be used to monitor equipment, crops, and livestock from a smartphone, and obtain statistics on livestock feeding and production.

After long enough use of IIoT in agriculture and a long enough collection of data in the field, statistical predictions for livestock and crops can be run. The use of sensors in greenhouses can also be used to create self-regulating microclimates conducive to crop production, which can be used with monitoring systems to generate data that can be applied to regulate crop spraying, irrigation, lighting, temperature, and humidity.

IIoT is being used in retail to help companies increase customer satisfaction through sensors connected to dashboards, allowing users to collect customer feedback after a shopping experience. They can also be used by retailers to monitor goods throughout an entire supply chain, reporting data such as location, temperature, humidity, shock, and tilt in order to offer quality control data and traceability. This can also help determine if materials are safe, delivered on time, and transported in ideal conditions. For food retailers, the monitoring of storage spaces and the conditions of those spaces is especially important to ensure the food does not spoil. Retailers offering shopping carts or baskets can also use integrated IoT sensors to track these assets and ensure they do not lose them, with the ability to track them to an exact location and receive updates or alerts if damaged or otherwise compromised.

Collected IoT data can also be used to offer customer-specific content and products based on the data tracked and developed from customer habits. As well, retailers can use this data to improve product maintenance, features, and design, based on customer feedback and user statistics. In-store IIoT can collect data to understand the wait times at cash. These wait times often lower customer retention through wait-time frustration. But using IIoT, retailers can offer customers a real-time prediction of the amount of time they can expect to spend waiting, and help stores manage those wait times. As well, through wearables or programs installed on wearable devices, retailers can identify loyal customers and work to offer personalized programs and services based on their shopping habits.

The use of Industrial IoT in healthcare can increase access to remote quality of care, operational efficiencies, and an opportunity to optimize existing products and services while also creating new products and services. This can include using mobile hardware connected to the cloud for real-time data transfers, asset tracking for locating equipment and staff, patient monitoring for access to patient status and information from anywhere, and automated data transfers to ease data movement from responsible parties.

The use of IIoT to generate data from healthcare can offer a chance to develop patient histories; track patient behavior; develop better healthcare and patient insights through analysis of the data; and manage clinical data, such as admissions, transfers, diagnosis information, overall hospital patient volume, length of stay, overtime tracking for physicians and employees, and patient satisfaction scoring.

Used in agriculture for increasing worker safety in industrial environments and for monitoring environmental conditions for pollutants and other harmful chemicals, IIoT has been used for environmental monitoring in various verticals. This includes in infrastructure, inventory management, precision farming, and the monitoring of pollutants in the environment or for weather. The collection of data through IIoT offers a chance to develop better understanding of the conditions in a specific area, develop predictive models (such as in the use of IIoT sensors to predict weather patterns) and to monitor industrial environments for workplace safety. Environmental monitoring is a growing application class for IIoT devices.

The application of industrial IoT things in a smart grid framework for optimization and efficiency as well as with an integration of renewable energy sources can decrease the use of energy and the over-production of energy. These technologies can also, and have been used to, optimize energy use for industries to reduce the overall draw on an energy grid. And, further use of technologies for energy use and automated energy use for homes, and the capability for those devices to communicate to the grid, can further optimize and reduce the energy demands on the grid.

The use of IoT devices has increased overall energy draw; however, as more devices use an energy-efficient architecture, which consists of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications, this can reduce the energy requirements of IoT systems and offer a longer lifetime for the devices as well.

There are various ways IoT can offer an increase in sustainability through simple improvements in optimization of machines and the performance of those machines along a production line. This can increase the performance of relevant technologies without an equivalent increase in power draw. This has offered industries, such as grocery retailers, a chance to reduce food waste. Some believe the use of IIoT can offer a holistic understanding of waste, especially along a supply chain, allowing industries to better manage supply chains, reduce related waste, and increase overall sustainability.