Industrial IoT

With the increase ofin 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.

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 networksnetwork gateways do a part of the data processing and analytics, andanalytic; 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.

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 deviceBluetooth-connected differsdevices. 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.

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 scenariosscenario 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 verticals of IIoT continue to expand as more uses for smart devices or connected devices are explored and used. 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.

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 buildingsbuilding'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, and 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 that,; 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, and optimize office layouts for emerging workforce trends, and offer floor traffic data to help users enhance offerings during peak hours.

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 whichthat can be applied to regulate crop spraying, irrigation, lighting, temperature, and humidity.

IIoT is being used in retail to help companies to 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 orof 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.

CollectingCollected 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 timereal-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 also offer a chance to develop patient histories and; track patient behavior,; develop better healthcare and patient insights through analysis of the data,; and the management ofmanage 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 use of IoT devices havehas 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. And, there is the belief by some,Some thatbelieve 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.

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 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 vary frominclude low-power boards and single board processors, to; data acquisition modules working to acquire physical things monitored by the hardware,; toand the hardware needed to process the data either in the IIoT device or, once communicated, in a main or cloud-based platform.

Sensors used in IIoT offer monitoring uses 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.

With the increase of 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 interactsinteract.

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 against each other. 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.

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.

ThisIIoT 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
• or 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 vary from low-power boards and single board processors, to data acquisition modules working to acquire physical things monitored by the hardware, to 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 includesinclude any hardware that works to connect devices at any point in a network or ecosystem. ThisThese can include gateways, routers, and platforms. The hardware used will depend on the network type or; solutions used; and based on 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 offersoffer monitoring uses 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 sensors. WhichsensorsThese 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, workworks 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.

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

Then there are cloudcloud- and fog basedfog-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 networks gateways do a part of the data processing and analytics, and 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.

Edge software platforms for IIoT performsperform 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 productsend-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 over internet-connected devices versus that required for Bluetooth connected device differs. And, moreoverMoreover, 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. As well, especially withWith the different connectivity standards and, different connectivity protocols, and the general lack of security on the devices themselves, and 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 scenarios 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, further, 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. And the needThe 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, in turn have leadled 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 increaseuptick 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 drive ndriven by advances in artificial intelligence, machine learning, and deep learning when combined with the bandwidth and network capabilities of 5G.

Beyond manufacturing of automobiles, the implementations for IIoT in the automotive industry are everywhere. This can include in fleetin-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, as well aswhen paired with wearable devices which that can monitor workers' health and risk, IIoT can create a safer workplace. 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.

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 networkaccess 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 drive n by advances in artificial intelligence, machine learning, and deep learning when combined with the bandwidth and network capabilities of 5G.

Then there are cloud and fog based architectures. In both of these architectures the data processingdata 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 networks gateways do a part of the data processing and analytics, and 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.

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 networks gateways do a part of the data processing and analytics, and the layers include monitoring, preprocessing, storage, and security between the physical and transport layers. Often fog and edge computingedge 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.

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 to communicate against each other. And often these types of models are similar to studying human social networkssocial 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.

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 drive n by advances in artificial intelligence, machine learning, and deep learning when combined with the bandwidth and network capabilities of 5G.

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.

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 networks gateways do a part of the data processing and analytics, and 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.

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 to communicate against each other. 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.

The verticals of IIoT continue to expand as more uses for smart devices or connected devices are explored and used. 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 chainsupply chain logistics, grow food, take care of our health, and manufacture goods.

IIoT implementations can use artificial intelligence to enhance big databig 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, provides the ability to make raw data meaningful and useful for decision making purposes.

The application of industrial IoT things in a smart gridsmart 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.