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What is the Internet of Things (IoT)?

In the most general terms, the Internet of Things includes any object – or “thing” – that can be connected to an Internet network, from factory equipment and cars to mobile devices and smart watches. But today, the IoT has more specifically come to mean connected things that are equipped with sensors, software, and other technologies that allow them to transmit and receive data – to and from other things. Traditionally, connectivity was achieved mainly via Wi-Fi, whereas today 5G and other types of network platforms are increasingly able to handle large data sets with speed and reliability.

 

Of course, the whole purpose of gathering data is not merely to have it but to use it. Once IoT devices collect and transmit data, the ultimate point is to analyze it and create an informed action. Here is where AI technologies come into play: augmenting IoT networks with the power of advanced analytics and machine learning.

 

Internet of Things definition: connected objects and devices (aka “things”) that are equipped with sensors, software, and other technologies that allow them to transmit and receive data – to and from other things

IoT devices are empowered to be our eyes and ears when we can’t physically be there. Equipped with sensors, devices capture the data that we might see, hear, or sense. They then share that data as directed, and we analyze it to help us inform and automate our subsequent actions or decisions. There are four key stages in this process:

 

  1. Capture the data. Through sensors, IoT devices capture data from their environments. This could be as simple as the temperature or as complex a real-time video feed.
  2. Share the data. Using available network connections, IoT devices make this data accessible through a public or private cloud, as directed.
  3. Process the data. At this point, software is programmed to do something based on that data – such as turn on a fan or send a warning.
  4. Act on the data. Accumulated data from all devices within an IoT network is analyzed. This delivers powerful insights to inform confident actions and business decisions.

In 2019, IoT devices generated about 18 zettabytes of data, and by 2025, the IDC expects that number to more than triple to over 73 zettabytes – which is equal to 73 trillion gigabytes. Although we can’t really quantify digital data in physical terms, we can say that if all that data were converted into 1990s floppy disks – and they were laid out end to end – they could go to the moon and back over 5000 times. For IoT to evolve, a specific set of technologies had to come together and advance concurrently.

 

  • Connectivity: This enormous growth in IoT data volume could only have happened with sufficiently robust Internet and cloud connectivity to send and receive it. Currently, many IoT devices are dependent upon a local Wi-Fi network for its capacity to transmit complex and voluminous data. But as 5G and other cellular networks improve, a recent article from McKinsey outlines the impact that may have and how it may untether IoT devices from Wi-Fi networks.
  • Sensor technology: With the steady rise in demand for IoT sensor innovation, the market went from a few costly, niche providers to a highly globalized and price-competitive sensor manufacturing industry. Since 2004, the average price of IoT sensors has dropped by over 70%, accompanied by a demand-fueled rise in better functionality and diversity in these products.
  • Computing power: There will be two times more data created in the next five years since the start of digital storage. To use and leverage all that data, modern businesses demand ever-increasing amounts of memory and processing power. The race to achieve this has been fast and competitive and has driven the growing relevance and applicability of IoT.
  • Artificial intelligence and machine learning: These technologies give businesses the ability to not only manage and process vast amounts of IoT data but to analyze and learn from it. Big Data is the favorite food of AI and machine learning. The bigger and more diverse the data sets, the more robust and accurate the insights and intel that AI-powered advanced analytics can deliver. The rise in IoT devices has very much grown alongside the advancement of artificial intelligence and its appetite for the data they deliver.
  • Cloud computing: Just as connectivity was integral to the development of the Internet of Things, the rise of cloud computing has also been closely tied to its evolution. With the ability to deliver processing power and high-volume storage on demand, cloud IoT services paved the way for IoT devices to gather and transmit increasingly large and complex data sets. Private cloud solutions have also made it possible for businesses to manage greater volumes and types of IoT data while maintaining the security of a closed system. 
  • Edge computing: Devices within an IoT network are often widely disbursed geographically, yet they all transmit data to a single, central system. As IoT data volumes grow increasingly massive, they can begin to monopolize a company’s bandwidth and cloud capacity. Furthermore, data takes time to be captured, transmitted, processed, and received at its final destination. This lag – known as “latency” – adds further inefficiency, especially to businesses where data processing is highly time-sensitive. Edge computing solutions decentralize a system’s processing power by bringing it closer to the source of the data. This is accomplished with the integration of localized computing systems, as well as building processing capacities into the IoT devices themselves. This processed data drives immediate action on-site and is then sent periodically – in a more structured and organized format – to the central system where advanced analytics and processing can take place.

IIoT refers to the use of connected machines, devices, and sensors in industrial applications. When run by a modern ERP with AI and machine learning capabilities, the data generated by IIoT devices can be analyzed and leveraged to improve efficiency, productivity, visibility, and more. IIoT networks typically support machine-to-machine (M2M) communication, and, as well as transmitting data, IIoT-integrated devices also regularly receive automation programming from the central system.

IIoT definition: IIoT refers to the use of connected machines, devices, and sensors in industrial applications

We are now in the midst of the Fourth Industrial Revolution – also known as Industry 4.0. The “revolution” in each of the past three industrial eras was driven by game-changing technologies. In the First Industrial Revolution, it was steam power; in the Second, the assembly line and mechanized production; and in the Third, computing power. The revolution that underpins Industry 4.0 comes in the form of industrial digitization and cyber-physical systems – and IoT is at its foundation.

The differences lie less in how they work and more in how they are used. The bulk of the world’s IoT solutions tend to have individuals as their end users and are commonly seen in things like smart appliances, digital assistants, or geo-locators on our phones.

 

IIoT is a subset of IoT, and, while it is driven by the same basic technologies, its focus is much more on automation and efficiency across an entire, connected organization as opposed to an isolated user. In IIoT networks, gathering and curating data is only the first step in a more complex process. To provide maximum benefit to an organization, artificial intelligence and machine learning must be applied to that data to deliver accurate insights and optimize workflows and automated tasks.

  • Smart manufacturing: Businesses gather data from customer feedback, media trends, and the global market. AI-powered systems can amalgamate this and other relevant data to inform product development and quality control. Based on such insights, an IIoT network of machines and robotic devices can be automated to optimize product manufacturing in smart factories.
  • Resilient supply chains: IIoT networks – and the AI-powered systems that run them – let supply chain managers know things like where their products are, which suppliers have them, and how many are in stock. IIoT devices and machines can also be programmed on the fly to adapt to real-time events and disruptions, giving businesses built-in contingency planning and a competitive, resilient edge.
  • Intelligent logistics: The Amazon Effect is a term that describes consumers’ growing expectation for free, next-day delivery on practically anything they order. To compete and fulfill this expectation, logistics providers have had to scatter their inventories geographically and bring on third-party logistics (3PL) partners. IoT solutions in a logistics network help managers keep a centralized view of every vehicle in their fleet – be it drone or cargo ship. Real-time data from IoT sensors can help to amalgamate loads, minimize waste, and speed up deliveries.
  • Healthcare: From the patients’ perspective, IoT monitors and wearables can help them feel more in control of their care, all the while connected to their healthcare provider. For medical practitioners, the data delivered by these devices can give a more complete picture of patient health. The result is a more informed and thorough approach to diagnostics, treatment, and general well-being.
  • Agriculture: For businesses dependent upon weather and natural forces, any tool that helps reduce risk and vulnerability is a welcome addition. Forbes magazine points out that the modern agricultural sector is increasingly adopting IoT solutions and that “There are thousands of sensors currently deployed to improve water sustainability, imaging, production and ease of farming.”

As part of an overall process of digital transformation, an IIoT network provides a powerful tool for building greater resilience and competitiveness.

 

  1. Improved agility: When IIoT devices share data in real-time, they contribute to an intelligence network that continually gathers, analyzes, and learns from data. This allows businesses to respond to opportunity – and risk – with speed and decisiveness. And those same devices not only send data but can also receive instructions based on data analysis to adapt and optimize their automated workflows.
  2. Healthier machines: Devices and machines in an IoT network are continually transmitting operational logs and performance data. AI and machine learning algorithms use this sensor data to gain valuable predictive maintenance insights. In fact, according to McKinsey, “Predictive maintenance typically reduces machine downtime by 30% to 50% and increases machine life by 20% to 40%.”
  3. Greater efficiency: Unfortunately, “if it ain’t broke” is often the stance that businesses take when prioritizing their operational needs. This attitude can lead to inefficient legacy processes hanging on past their prime. When an operational network incorporates IoT devices, the data they gather and transmit is entirely objective. The application of advanced analytics to such data leads to ongoing recommendations and strategies for updating processes, streamlining tasks, and achieving increased efficiency.
  4. Smarter inventory management: At the start of 2020, U.S. businesses had already spent a few years weathering political and trade uncertainties. For many, the pandemic only served to drive home just how vulnerable and reactive their inventory management systems had become. When connected to an IoT network, devices, such as additive (3D) printers can reduce dependency upon external manufacturing partners and allow businesses to retain virtual inventories and manufacture the products they need – on demand.
  5. Safer workers: In any industrial setting, there is always the danger of injury or strain. Today, many businesses are reducing this risk with the use of IoT workplace safety devices. These may deliver warnings via wearable units such as VR headsets or monitor ongoing workplace patterns to restructure factory and warehouse floors to be safer and more ergonomic.
  6. Improved customer service: IIoT networks connect more than just the devices and machines within a business – they also integrate the customer’s experience and input.  This integration results in more seamless shopping experiences, more transparent and personalized logistics, and greater ability to incorporate customer feedback and preferences into the manufacturing and development of new products.  Real-time and meaningful engagement with customers leads to a more competitive and resilient business model.

In 2020, many businesses got a sharp reminder of the importance of resilience and visibility across their entire network of operations. The companies that are competing – and thriving – in the modern economy are no longer looking at digital transformation as something “nice to have” down the road. Today’s best businesses embrace modern digital solutions, like IIoT, as necessary tools for achieving success and growth.

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