How Data & Analytics Are Advancing IoT and RFID
The Internet of Things (IoT) has changed the way people interact with their environments. From smart homes to digitally enabled buildings, factories and communities, IoT is driving advances across all layers of society. In 2022 alone, the IoT market is expected to grow 18 percent to 14.4 billion active connections, according to IoT Analytics. By 2025, that number is expected to be 27 billion connected IoT devices. It’s a sign of just how pervasive IoT has become in our lives. It would be impossible to list all of the current and potential applications. It’s as infinite as human ideas.
What is IoT and how is it delivering data that helps with next-generation RFID applications? Let’s look at how IoT and RFID are related, the use of data science to extract insights and how those insights are opening new opportunities that are transforming nearly every sector and industry.
What Is IoT?
IoT is used to describe interconnected devices, machines and objects that collect and share data via the Internet. Gartner describes IoT as “the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment.” That network is made of a device equipped with one or more sensors, software and Internet connectivity to facilitate the collection and exchange of data.
IoT devices can be nearly anything. For example, robot vacuums and thermostats are everyday objects in our homes that can be accessed and controlled remotely. IoT technology has an exploding number of business applications too. From the monitoring of industrial machines and aircraft engines to tracking bulk items like multiple objects in boxes or pallets of materials and much more.
How Are IoT and RFID Related?
The evolution of IoT overlaps with the advancement of RFID and sensor technologies. The term was coined in 1999 and the meaning of the term has evolved over time. RFID is now a foundational technology for IoT ecosystems, by allowing physical objects to communicate with an interconnected network. RFID tags can be read automatically, in groups and from a distance making it possible to connect devices to the internet and monitor them remotely. Essentially, RFID tags make physical objects communicate by reporting on a potentially infinite number of variables.
IoT Data – Applying Data Science for IoT Analytics
The IoT market continues to grow rapidly and the more devices we connect to the Internet, the more data is created. Terabytes upon terabytes of it. Examples of IoT data are everywhere. We see it in connected appliances and smart home security systems collecting valuable information on system performance, user behavior and user preferences. Wearable health monitors build a baseline understanding of a person’s physical wellness that can help improve health outcomes. Smart industrial sensors generate data on equipment health, productivity and workflows, enabling managers to identify and remediate sticking points in production. All the data generated by IoT can help to drive improved business outcomes, fine-tune operations and deliver a higher level of service for employees and customers.
Although objects and devices are made “smart” through the application of IoT, the data delivered doesn’t arrive neatly packaged. Rather it is big – and sometimes unstructured – data. The discipline of data science is offering help to bring IoT data to life by inspecting, cleansing and modeling data in order to discover insights, trends and patterns that drive informed decision-making. Employing Artificial Intelligence (AI) and Machine Learning (ML) assists in predicting the behaviors of monitored assets. By leveraging and learning from usage and behavior patterns derived from IoT tags and sensors, anomalies can trigger notifications if equipment health appears to be at risk or optimize building systems when occupancy thresholds are reached.
Putting IoT Sensor Data to Work – IoT Solutions
IoT data are driving the evolution of IoT solutions that deliver on a range of benefits. For example, IoT solutions can help to determine the location and status of goods, streamlining operations and lowering operational costs. Those efficiency gains and savings include real-time asset production and shipment tracking, warehouse capacity optimization and more. Public transportation applications include contactless payments, smart ticketing, mobile ticketing and wayfinding.
IoT data can drive improvements in operations monitoring. In technology-driven organizations, analytics can yield insights that help IT leaders to deploy, maintain and troubleshoot networking devices as well as connected equipment for predictive maintenance or to reduce energy costs, according to CIO magazine. In healthcare, IoT data empowers data-driven location and monitoring of clinicians, patients and devices, giving increased visibility into operational efficiency and elevating everything from clinical accountability and asset management. A Translational Pulmonary and Immunology Research Center case study highlighted how the clinic utilized a real-time location system to maintain continuity of care during the pandemic.
In workplace settings, IoT data is increasingly the backbone of staff health and safety. In a factory, for example, wearable sensors can be used to track work patterns, with analytics serving to identify potential areas of risk. Location-based IoT applications help to make smart buildings safer. They may enable faster response times in case of emergency, for example, giving location-based data on a possible hazard or a questionable environmental condition. Such is the case with Johnson Controls use of real-time location services, which was used to support health and safety measures through its OpenBlue building management platform.
Powering the Future – IoT Data and RFID
Business has generated and processed data for a long time, but the arrival of IoT has changed the game. What is on the horizon when it comes to innovation through IoT and RFID? What capabilities will be needed?
As RFID tag memory increases (and size decreases), RFID will become a part of a holistic ecosystem of sensors and communication technologies. With advanced IoT applications, integrating RFID sensors into systems to automate and streamline tasks that used to require a mediating technology or intervening human. This means data analytics will remain a crucial element and data science will evolve models and methodologies to make it more powerful and accurate. Edge computing, deep learning and sensor fusion will see widespread adoption. Leveraging these new technologies and deploying data science is more important than ever as organizations strive to process and analyze the billions of real-time data points collected and organize them into insights that drive process optimization.
Brad Williams is the Global Sales Director focusing on industrial markets for IoT Services. Prior to HID, Brad spent 3.5 years with Ubisense and 18 years with Brady Corporation, where he held various Strategic Sales, Market and Account Leadership roles. His 20+ year track record include achievement in sales, business development, key account management, and strategic account planning in industrial market sectors such as Aerospace, Auto, CVAM, Medical and Engineering services.