At its core, RFID has always been about answering a simple question: what is this thing, and where is it? IoT sensors, on the other hand, track environmental conditions like temperature, humidity, vibration, and location in real time. When you combine tag data with sensor networks, you move beyond identification into a world of contextual awareness. You do not just know that a pallet of pharmaceuticals left the warehouse. You know the temperature it experienced at every stage of transit, whether it was exposed to excessive moisture, and exactly when it arrived at its destination.

This combination of data streams is already transforming supply chain management, healthcare logistics, and manufacturing quality control. In cold chain monitoring, for example, UHF RFID tags paired with IoT temperature sensors create an unbroken record of product conditions from origin to point of sale. If a shipment of vaccines drifts outside the acceptable temperature range, automated alerts trigger before the product reaches the end user. That kind of real-time visibility was nearly impossible just a few years ago.

Edge computing plays a critical role in making this work at scale. Rather than sending every tag read and sensor measurement back to a central cloud platform, edge devices process data locally, filtering out noise and acting on events as they happen. An edge gateway at a loading dock might correlate RFID scan events with weight sensor data, flagging discrepancies instantly rather than waiting for a batch upload. This reduces latency, lowers bandwidth costs, and keeps operations moving even when connectivity drops.

The integration of RFID and IoT also lays the groundwork for digital twins. A digital twin is a virtual representation of a physical asset that updates in real time based on incoming data. By feeding RFID identification events and IoT sensor readings into a twin platform, organisations can model the behaviour of individual products, machines, or entire facilities. Predictive maintenance becomes more accurate when you combine machine identity data from RFID with vibration and thermal readings from IoT sensors. You can spot patterns that point to failure long before a breakdown occurs.

Practical integration does not require a massive overhaul. Many businesses start by layering IoT sensors onto existing RFID infrastructure. Middleware platforms like MQTT brokers and event-driven architectures handle the merging of data streams, translating tag reads and sensor outputs into unified event feeds. Cloud platforms from AWS, Azure, and Google all offer IoT hubs that accept RFID data alongside sensor telemetry, making it straightforward to build dashboards, trigger workflows, and feed analytics engines.

The key to getting this right is thinking about the data model early. RFID gives you the “what” and “where.” IoT sensors give you the “how” and “when.” Bringing those together into a coherent data layer is what unlocks the real value, from automated compliance reporting to predictive logistics and beyond. Businesses that treat RFID and IoT as complementary rather than competing technologies are the ones pulling ahead.

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How the RFID-Based System Works

The system uses small, patch-like passive RFID tags placed directly on the patient’s body. Because these tags are passive, they require no batteries and instead harvest energy from a nearby RFID reader. The reader transmits radio waves that power the tags and capture their precise movements, allowing clinicians to monitor breathing patterns across multiple body locations at the same time, including the chest and abdomen.

This simultaneous multi-point measurement is a key advantage over many existing methods, as it gives a more complete picture of how the respiratory system is functioning during each breath cycle.

Addressing Limitations of Traditional Respiratory Monitoring

Conventional methods for assessing lung function often rely on imaging technologies such as X-rays and CT scans. These require dedicated hospital equipment, can expose patients to ionising radiation, and are not suitable for continuous or long-term monitoring outside a clinical environment.

The RFID-based approach sidesteps many of these drawbacks. It is wireless, portable, safe for repeated use, and does not require complex installation. This makes it particularly well suited for patients who need ongoing respiratory monitoring, such as those recovering from lung surgery or managing chronic respiratory conditions.

Xuezhi Zeng, Associate Professor at Chalmers’ Department of Electrical Engineering, described the goal clearly: “The goal is to enable more personalised and evidence-based rehabilitation” for patients in these groups.

Early Testing and Results

Initial testing was carried out at the simulation centre at Sahlgrenska University Hospital, where a commercial RFID reader system was used alongside a medical mannequin fitted with RFID tags on the chest. The results were promising, with the system successfully detecting even minor variations in movement between different measurement points on the body surface.

The study has been published in IEEE Access and received funding from Chalmers’ Area of Advance Health Engineering.

The Road Ahead

The research team is now working toward developing a custom-designed prototype specifically intended for clinical use. According to Zeng, the team hopes to begin testing the prototype on real patients within five years.

Looking further ahead, the long-term vision includes enabling continuous respiratory monitoring in the home. This could allow healthcare providers to detect early signs of respiratory deterioration in at-risk patients before a condition becomes serious, potentially speeding up the time to diagnosis and treatment.

For the many patients living with chronic lung disease or recovering from thoracic surgery, a lightweight, wearable, battery-free monitoring system could represent a significant improvement in quality of care and independence from hospital settings.

Read more at https://www.chalmers.se/en/current/news/e2-wireless-technology-could-open-up-new-possibilities-for-measuring-respiratory-function/

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At the heart of this collaboration are two innovative tags developed by Paragon ID, both of which operate without batteries and remove one of the biggest barriers to deploying tracking at scale.

The standout device is the XgenTag-R, which harvests energy directly from UHF RFID fields. This tag activates within 20 metres of standard UHF readers, drawing power from the RF energy already present in the environment. This makes it particularly well suited for tracking assets that are enclosed, hidden from view, or stored in areas where light-based energy harvesting is impractical. The ability to power a sensor and transmit data using nothing more than ambient UHF energy is a genuinely innovative approach. It means organisations can collect data on a massive scale without worrying about battery life, replacement schedules, or the environmental cost of disposable power sources. The potential applications are enormous, from monitoring thousands of pallets in a warehouse to tracking surgical instruments inside hospital storage units.

Alongside the UHF-powered tag, Paragon ID has also developed the XgenTag-L, a Bluetooth Low Energy (BLE) tag that harvests ambient light through organic photovoltaic surfaces. It transmits a signal once per second under normal lighting conditions and can still function in near-darkness at just 5 lux. The designed operational life exceeds ten years, again without any battery replacement. Both tags combine location tracking with basic sensing capabilities, including temperature monitoring.

What makes this partnership particularly interesting is the network side. Nodle’s ConnectX platform turns millions of participating smartphones into passive relay infrastructure. When an XgenTag broadcasts its BLE beacon, nearby phones running the Nodle app capture the signal, determine location data, and relay the information back to the cloud. Users earn NODL tokens for their participation. This crowdsourced approach eliminates the need for dedicated gateway hardware or cellular contracts, dramatically reducing the cost of deploying tracking across wide areas.

The use of UHF RF energy to power sensors and collect data at scale could prove transformative across several sectors. In logistics, it enables real-time visibility of pallets, containers, waste bins, and kegs. In healthcare, it supports tracking of surgical instruments and pharmaceutical shipments where maintaining cold chain integrity is critical. Construction firms can monitor scaffolding and tools across sprawling sites, while aviation operators can track baggage carts and unit load devices on airport aprons.

Field trials are already underway across three continents. In France, deployments are running at a logistics facility in Albi and at Nice Airport. Trials are also active in South America, covering Santiago and Sao Paulo, as well as in Africa.

As UHF RFID infrastructure continues to expand globally, the ability to piggyback sensor-equipped tags onto existing reader networks represents a significant step forward. Rather than building entirely new tracking systems, organisations can layer intelligence onto infrastructure they already have, collecting environmental and location data without additional power sources or connectivity costs.

Read more at https://www.paragon-id.com/en/inspiration/nodle-and-paragon-id-join-forces-to-redefine-what-can-be-tracked

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The initiative tackles a longstanding industry challenge: applying RFID in high-moisture, cold-case environments. By creating sensor-enabled RFID labels that can operate reliably in refrigerated and wet conditions, the partners are enabling item-level tracking of fresh-food inventory for the first time.

With this technology, store associates at Walmart will be able to access digital use-by dates, monitor freshness and optimise product rotation with greater speed and accuracy. The ability to track each item’s digital identity means fewer manual tasks and smarter markdown decisions, which in turn helps reduce food waste and improve operational efficiency.

Julie Vargas, Vice-President and General Manager of Avery Dennison Identification Solutions, described the work as “first-to-market RFID innovation across multiple fresh-food categories” and emphasised the mutual commitment of both companies to people and the planet. Christyn Keef, Vice-President of Front End Transformation for Walmart U.S., added that the goal is to make technology work for associates and customers by reducing manual labour and allowing more focus on service. 

From a strategic perspective the collaboration aligns with Walmart’s broader sustainability objective of halving global operational food loss and waste intensity by 2030. By enabling automated item-level identification in fresh departments the retailer is integrating a new generation of supply-chain intelligence into its store operations. 

For Avery Dennison this development reinforces its position in the digital-identification domain by applying its Optica solutions portfolio to a complex and demanding environment. The move signals the expansion of RFID from apparel and general retail into high-moisture food segments.

Despite the promise there are implementation challenges ahead. Cold-chain conditions present unique demands for tag durability, readability and attachment methods. Store-floor workflows will need to adapt to integrate item-level data into associate tasks. And store systems will need to ingest and act upon fresh-food data in real time. Still the benefits are compelling: improved freshness perception for shoppers, reduced waste and tighter inventory control.

In conclusion this collaboration between Walmart and Avery Dennison marks a significant step in the evolution of retail food-technology. By bringing RFID into fresh food departments at scale the partners are raising the bar for traceability, stock management and sustainability in grocery retail. As item-level digital identities become embedded in meat, bakery and deli segments we may be witnessing a new era in store-operation intelligence.

See Avery Dennison Press Release

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