WiFi devices are ubiquitous in modern environments, from smartphones and laptops to IoT sensors and AR/VR headsets. Identifying device types/models within these populations enables crowd analysis, network optimization, and detection of unusual devices. Current identification methods struggle with MAC address randomization, require large training datasets, and perform poorly in real-world deployments. This paper introduces a device identification method based on Information Element (IE) attributes extracted from WiFi probe requests. We evaluate the approach using probe requests captured in the 2.4 GHz band. Evaluation across 70+ device types yields 99% precision, 98% recall, and 99% F1 score, exceeding deep learning approaches (92% F1 score) under similar training conditions. Our approach maintains accuracy despite MAC randomization and requires minimal training data. We demonstrate practical applicability through an operational dashboard tested in real-world scenarios for urban planning and network management. Case studies across diverse environments confirm the effectiveness of the method for operational use.

Mixed Reality systems for IoT control often rely on fragmented interfaces, cloud infrastructure, and lack spatial awareness. We present ViewIoT: a fully on-device framework that enables intuitive and spatially grounded interaction with nearby IoT devices using a head-mounted display. ViewIoT discovers and localizes devices by combining real-time 3D spatial meshes with lightweight wireless signal sensing, removing the need for manual setup or external infrastructure. At the core is a unified multimodal intent recognition pipeline, powered by an embedded language model, that interprets hand gestures, voice commands, gaze, and proximity signals to resolve user intent in under 100 milliseconds. Running on a Meta Quest 3S, our prototype supports real-time IoT control through gaze-based panels and natural interaction without relying on cloud services or pre-installed anchors. This work provides a practical foundation for seamless and infrastructure-free management of smart environments in a Mixed Reality setting.

WiFi has emerged as the standard method for local connectivity across various devices, including smart assistants, IoT devices, smart TVs, and AR/VR devices. Identifying WiFi devices in neighborhoods has implications for law enforcement, urban planning, and socio-economic analysis. This paper introduces a novel approach to constructing WiFi device-type signatures using Information Element attributes from wildcard WiFi probe requests. Our method accurately identifies device types even when dealing with randomized MAC addresses and requires minimal training data, thus addressing limitations of existing machine learning and deep learning approaches. We evaluate our approach using a dataset of 51,726 probe requests across 50 device types, achieving an average F1 score of 99%, precision of 99%, and recall of 98% in device-type identification. Importantly, our method outperforms deep learning methods with significantly less training data, achieving a 92% F1 score with only one training sample per device type.