Wireless Tracking Innovations

What if your home #WiFi could care for your loved ones? No wearables. No cameras. Just the existing WiFi signals in the house detecting if they fall or become inactive! In our latest work, we show how our #AI algorithm uses standard WiFi to track 2D #human #skeletons and detect #activities like #falls or inactivity, with accuracy close to camera-based systems, all while preserving privacy. This will be a large step forward in non-intrusive, intelligent elder care. - Paper: Younggeol Cho, Elisa Motta, Olivia Nocentini, Marta Lagomarsino, Andrea Merello, Marco Crepaldi, and Arash Ajoudani. "Wi-Fi based Human Fall and Activity Recognition using Transformer-based Encoder–Decoder and Graph Neural Networks" IEEE Sensors 2025. - Link to paper (open): https://lnkd.in/dpUB4gCS - Full video: https://lnkd.in/d9ji-P-h IEEE SENSORS Istituto Italiano di Tecnologia

🚀 Enabling Precise Positioning in 6G Networks Imagine a world where your smartphone, autonomous car, or drone can locate itself with sub-meter accuracy—even in complex urban areas with no GPS signal. This is a critical capability for the future of 6G wireless networks, powering everything from smart factories to augmented reality. In our latest research, "Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations", we introduce a new AI-based approach—SCADF-MoE—that achieves this level of precision by: ✅ Dynamically learning which signals are most reliable at different frequencies (2.6, 6, 28 GHz) ✅ Leveraging the spatial relationship between neighboring locations to reduce confusion in tricky environments ✅ Performing consistently—even in non-line-of-sight scenarios where traditional methods fail 📊 The results? A 63% improvement in accuracy and sub-meter localization, even under the hardest conditions. 💡 Why it matters: Accurate localization is the foundation of many 6G applications—like self-driving cars, digital twins, and robotics. Our work provides a scalable, AI-native solution that adapts across environments and frequency bands. Great collaboration with amazing colleagues from Zhejiang University, University of Michigan, and Khalifa University. You can read more here: https://lnkd.in/dnMj5abP #6G #AI #Localization #WirelessInnovation #SmartCities #AutonomousSystems #DeepLearning #MixtureOfExperts

Will future mobile networks do more than connect us, will they be able to “sense” the environment around them? 5G networks, with wider channel bandwidth options, brings the possibility of more accurate device positioning. However, future networks are considering going far beyond this, with technologies such as RF sensing and Integrated Sensing and Communication (ISAC) being explored for 6G. RF sensing uses radio frequency signals to detect and analyse objects and movements within an environment by measuring changes in the radio waves' propagation, reflection, and scattering. This technology extends beyond accurate positioning by gathering detailed data on object movement, speed, and even material properties. ISAC represents a broader integration of sensing and communication technologies. It combines RF sensing with other types of sensors, such as cameras, LIDAR, and motion sensors, to create a comprehensive system capable of both high-precision communication and detailed environmental sensing. This integration enables networks to function as both communication platforms and environmental sensors, opening up new possibilities and use cases. An application within ISAC is Simultaneous Localisation and Mapping (SLAM), which uses data from these diverse sensors to create real-time 2D and 3D maps of the environment while simultaneously determining the precise location of objects and devices, enhancing navigation and positioning capabilities (see paper in comments). 3GPP (TR 22.837) calls out 32 potential use cases for ISAC, such as smart homes, flood detection, factory floor ground vehicle detection, tracking and collision avoidance, and use cases for Unmanned Aerial Vehicles (UAVs). Nokia Bell Labs has called this area “Network as a Sensor” and is working with Bosch on 5G positioning trials along with considerations for ISAC. Qualcomm has tested and simulated RF sensing over an outdoor 28GHz network. What are your thoughts on the likelihood of this being a real use case for networks in the future? Anyone have any other details of trials in this area? #technology #6G #innovation #sensors #telecoms [Video: Qualcomm]

🛰️ GPS was yesterday: New IoT tracking beats the classic in terms of accuracy, range and energy consumption 📍#GPS is the benchmark but not the solution for everything. In the world of IoT, every microwatt and every metre of accuracy counts. If you want to operate sensors with a 10-year battery life, you need alternatives to traditional satellite navigation. Research from Ilmenau and solutions from orbit show that #tracking is now more precise, economical, and even #indoor. 📏 GPS as a benchmark - with clear limits GPS receivers are standard in many IoT devices. But: - Accuracy: 3-5 m outdoors - Energy consumption: 20-30 mA in active mode - Receiving: usually not possible indoors - Cold start times: >30 seconds with poor signal For many IoT applications - e.g. smart buildings, logistics hubs or underground car parks - this is not practical. LPWAN tracking with LTE time standard: precision of less than half a metre A research team from TU Ilmenau and FAU Erlangen-Nuremberg has set up an LPWAN testbed that analyses IQ data from several radio stations centrally via cloud RAN. Synchronisation takes place via LTE BTS - precisely and stably. Results from Ilmenau: - Synchronisation with LTE time: ±200 ps - Positioning accuracy: 10-15 cm - GPS time standard for comparison: 1.5-3 m - With Rubidium reference: < 10 cm (theoretically ~6 cm) And all this with signals in the SubGHz range - ideal for low-power LPWAN devices with high building penetration. 🛰️ Antennity: #Mioty NTN tracking from 500 km - even indoors #Antennity takes a global approach: IoT devices are located via satellites - even deep inside buildings. The signals from the LPWAN end devices reach satellites at an altitude of 500 km without GPS. Orbit tracking: - Accuracy: ~700 m, even indoors - Energy consumption: 3 uA - Operating time: up to 20 years on battery - Areas of application: global logistics, agriculture, offshore, asset tracking In contrast to GPS, this tracking also works where satellites do not provide receiving and where #LPWAN networks do not require a ground gateway. 🚀 Conclusion: Anyone planning IoT tracking today needs to think beyond GPS IoT needs flexible tracking depending on the use case. The future is hybrid: terrestrially precise, orbitally global with Mioty NTN. Mioty NTN is the ideal complement for 4G, 5G, 6G and 7G. Mioty has already been integrated into Tetra and DAB. It is independent of the PHY, independent of the frequency range and a successful LPWAN technology from Europe. You can talk to the inventors of Mioty and users at the free Mioty Summit on 15 May in Nuremberg. 📬 More of this? Follow the hashtag #IoTDiary Subscribe to my #IoT #M2M Times newsletter on LinkedIn. For #antenna design or #IoT consulting: harald.naumann(at)antennity .com Share this post - Knowledge is for sharing! Like👍 | Share ➡️ | Comment 💬 | Stay Informed 📚 | Register to our webinar ‘Beyond LPWAN’🖥️ | Place a pre-order for the LPWAN Cookbook 📖 | Talk to us at Mioty Summit 🤝

Researchers at La Sapienza University of Rome have developed "WhoFi," a novel surveillance system capable of identifying and tracking individuals using only standard Wi-Fi signals, without the need for cameras or personal devices. The technology works by analyzing how wireless waves reflect off a person's body, creating a unique biometric "fingerprint" that allows for re-identification across different rooms and locations. Utilizing neural networks, WhoFi achieves high accuracy and can even operate through walls and in complete darkness, presenting vast applications for security and retail analytics but also posing significant privacy risks as it can function without an individual's knowledge or consent https://lnkd.in/gaVnTkiq

🚨 Plot twist: That location signal you thought came from GPS? Yeah… it’s actually your WiFi. 😏 Let’s settle this once and for all: Most people think tracking assets or goods always relies on GPS. But in many of our pooling applications, WiFi scanning is the real hero 🦸 — and here’s why: 📡 WiFi Scanning ≠ Connecting to WiFi We’re not logging in — we’re just scanning the environment. Devices detect nearby routers (SSID + signal strength) and triangulate location. It’s faster, uses less battery, and even works indoors (where GPS fails miserably — looking at you, warehouse corners 👀). ⚡ Lower Energy, Longer Life GPS modules are energy vampires. For pooled assets with long life cycles and limited power, WiFi-based location is a game changer. 🏭 Better Contextual Awareness WiFi signals can tell us where an asset is, but also what environment it’s in — warehouse vs. retail vs. on the move. 🧠 More Data = Smarter Systems With enough signal data, AI models can estimate location with surprising accuracy — often within a few meters. No satellite needed. No sky view required. 😂 So next time someone says, “Just use GPS for tracking”, kindly remind them: “Using GPS indoors is like using a sundial in a cave.”