Three Cyberspace Applications for IoT RF Cloud in Localization, Motion Detection, and Security

Ying, Julang

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Three Cyberspace Applications for IoT RF Cloud in Localization, Motion Detection, and Security

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Internet of Things (IoT) has emerged as a new trend to provide novel technical solutions to cyberspace applications. The IoT network mainly consists of small wearable or implantable sensor nodes, using a variety of RF technologies such as WiFi, Bluetooth, ZigBee, and Ultra-Wideband (UWB). The RF signals radiating from these devices create a RF cloud that we can benefit from to design novel cyberspace applications such as positioning, motion and gesture detection and security. Currently, the most popular indoor geolocation technique in smart devices is the RSS-based Wi-Fi positioning. This technology takes advantage of existing RF cloud from Wi-Fi infrastructure deployed for wireless communications by fingerprinting the RF signals radiated from these devices. With the emergence of IoT, low power devices with diversified power levels are deployed with a higher density, which enables precise indoor geolocation with IoT RF cloud without a need for time consuming fingerprinting. Motions of objects close to the wireless devices cause temporal fluctuations of characteristic of RF cloud. These characteristics introduce a variety of features that we can benefit from for activity, motion, and gesture detection. Secure transmission of data is another major unsolved concern in IoT networks, with the demand of a practical authentication policy. We can also benefit from RF cloud of IoT devices to create secure communications. In this dissertation we study three novel examples of cyberspace applications of IoT RF cloud in localization, motion detection and security key generation. (1) We explore how IoT devices with diversified power level can affect the localization performance in dense IoT environment. We apply probability of coverageinto the empirical CRLB calculation and show how low power devices can improve the positioning precision and eliminate the need for expensive fingerprinting. (2) We extract both temporal and spatial IoT RF cloud characteristics and use these features for motion detection. Different detection approaches have been tested, and we conclude that RF cloud information can improve the detection accuracy. (3) We use multipath propagation characteristics from UWB sensors and generate a shared security keys using PHY-based schemes. Our analysis demonstrates the spatial performance of RSS-based schemes and TOA-based schemes from the aspect of Bit Match Rate (BMR), Key Generation Rate (KGR), and scalability, which opens possibilities for new RF solutions to IoT network security

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