Abstract:Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments.
Wireless information networks have become a necessity of our day-today life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics.
With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a modelbased RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.
Currently, the most popular indoor geolocation technique used in smart device is the RSS-based Wi-Fi localization. The general accuracy of Wi-Fi localization in around 10-15 m. To improve this accuracy in indoor areas, site survey is commonly used to collect a signature data base. With the emergence of smart buildings and the IoT to connect the sensors for the smart building, a number of low power sensor devices with diversified power levels will be deployed in the building. The radiated RSS from these devices can be used to improve the precision of the RSS based localization. In this paper we introduce an analytical frame work for calculation of the CRLB in an environment with variable radiated powers from sensors to complement the Wi-Fi localization. We use the analytical framework to demonstrate the accuracy of localization in the third floor of the Atwater Kent Laboratory at Worcester Polytechnic Institue, for existin Wi-Fi localization and variety of sensor deployment scenarios. The existing CRLB calculation for the RSS-Based localization use the differential value of the received signal strength from given reference points to calculate the variance of the location estimate. As a result, effect of the transmitted power from the sources is eliminated. In another words this approach cannot demonstrate the effects of transmitted power of the reference device on the performance of the system. By introducing the concept of certainty in the measurement of the RSS, we also introduce a novel model for calculation of the performance of RSS based localization with inclusion of the effects of the transmitted power from the reference sources.
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