Secure short-range communications

Analysts predict billions of everyday objects will soon become ``smart’\u27 after designers add wireless communication capabilities. Collectively known as the Internet of Things (IoT), these newly communication-enabled devices are envisioned to collect and share data among themselves, with new devices entering and exiting a particular environment frequently. People and the devices they wear or carry may soon encounter dozens, possibly hundreds, of devices each day. Many of these devices will be encountered for the first time. Additionally, some of the information the devices share may have privacy or security implications. Furthermore, many of these devices will have limited or non-existent user interfaces, making manual configuration cumbersome. This situation suggests that devices that have never met, nor shared a secret, but that are in the same physical area, must have a way to securely communicate that requires minimal manual intervention. In this dissertation we present novel approaches to solve these short-range communication issues. Our techniques are simple to use, secure, and consistent with user intent. We first present a technique called Wanda that uses radio strength as a communication channel to securely impart information onto nearby devices. We focus on using Wanda to introduce new devices into an environment, but Wanda could be used to impart any type of information onto wireless devices, regardless of device type or manufacturer. Next we describe SNAP, a method for a single-antenna wireless device to determine when it is in close physical proximity to another wireless device. Because radio waves are invisible, a user may believe transmissions are coming from a nearby device when in fact the transmissions are coming from a distant adversary attempting to trick the user into accepting a malicious payload. Our approach significantly raises the bar for an adversary attempting such a trick. Finally, we present a solution called JamFi that exploits MIMO antennas and the Inverse-Square Law to securely transfer data between nearby devices while denying more distant adversaries the ability to recover the data. We find JamFi is able to facilitate reliable and secure communication between two devices in close physical proximity, even though they have never met nor shared a key

A 360 VR and Wi-Fi Tracking Based Autonomous Telepresence Robot for Virtual Tour

This study proposes a novel mobile robot teleoperation interface that demonstrates the applicability of a robot-aided remote telepresence system with a virtual reality (VR) device to a virtual tour scenario. To improve realism and provide an intuitive replica of the remote environment for the user interface, the implemented system automatically moves a mobile robot (viewpoint) while displaying a 360-degree live video streamed from the robot to a VR device (Oculus Rift). Upon the user choosing a destination location from a given set of options, the robot generates a route based on a shortest path graph and travels along that the route using a wireless signal tracking method that depends on measuring the direction of arrival (DOA) of radio signals. This paper presents an overview of the system and architecture, and discusses its implementation aspects. Experimental results show that the proposed system is able to move to the destination stably using the signal tracking method, and that at the same time, the user can remotely control the robot through the VR interface

Flexible and scalable software defined radio based testbed for large scale body movement

Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems

BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring

Wearable devices are increasingly becoming mainstream consumer products carried by millions of consumers. However, the potential impact of these devices is currently constrained by fundamental limitations of their built-in sensors. In this paper, we introduce radio as a new powerful sensing modality for wearable devices and propose to transform radio into a mobile sensor of human activities and vital signs. We present BodyScan, a wearable system that enables radio to act as a single modality capable of providing whole-body continuous sensing of the user. BodyScan overcomes key limitations of existing wearable devices by providing a contactless and privacy-preserving approach to capturing a rich variety of human activities and vital sign information. Our prototype design of BodyScan is comprised of two components: one worn on the hip and the other worn on the wrist, and is inspired by the increasingly prevalent scenario where a user carries a smartphone while also wearing a wristband/smartwatch. This prototype can support daily usage with one single charge per day. Experimental results show that in controlled settings, BodyScan can recognize a diverse set of human activities while also estimating the user's breathing rate with high accuracy. Even in very challenging real-world settings, BodyScan can still infer activities with an average accuracy above 60% and monitor breathing rate information a reasonable amount of time during each day

Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities

WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition

Enabling 5G Technologies

The increasing demand for connectivity and broadband wireless access is leading to the fifth generation (5G) of cellular networks. The overall scope of 5G is greater in client width and diversity than in previous generations, requiring substantial changes to network topologies and air interfaces. This divergence from existing network designs is prompting a massive growth in research, with the U.S. government alone investing $400 million in advanced wireless technologies. 5G is projected to enable the connectivity of 20 billion devices by 2020, and dominate such areas as vehicular networking and the Internet of Things. However, many challenges exist to enable large scale deployment and general adoption of the cellular industries. In this dissertation, we propose three new additions to the literature to further the progression 5G development. These additions approach 5G from top down and bottom up perspectives considering interference modeling and physical layer prototyping. Heterogeneous deployments are considered from a purely analytical perspective, modeling co-channel interference between and among both macrocell and femtocell tiers. We further enhance these models with parameterized directional antennas and integrate them into a novel mixed point process study of the network. At the air interface, we examine Software-Defined Radio (SDR) development of physical link level simulations. First, we introduce a new algorithm acceleration framework for MATLAB, enabling real-time and concurrent applications. Extensible beyond SDR alone, this dataflow framework can provide application speedup for stream-based or data dependent processing. Furthermore, using SDRs we develop a localization testbed for dense deployments of 5G smallcells. Providing real-time tracking of targets using foundational direction of arrival estimation techniques, including a new OFDM based correlation implementation

RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing

In this work we present RAPID, a joint communication and radar (JCR) system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. In contrast to most existing approaches for human sensing at millimeter-waves, which employ special-purpose radars to retrieve the small-scale Doppler effect (micro-Doppler) caused by human motion, RAPID achieves radar-level sensing accuracy by retrofitting IEEE 802.11ay access points. For this, it leverages the IEEE 802.11ay beam training mechanism to accurately localize and track multiple individuals, while the in-packet beam tracking fields are exploited to extract the desired micro-Doppler signatures from the time-varying phase of the channel impulse response (CIR). The proposed approach enables activity recognition and person identification with IEEE 802.11ay wireless networks without requiring modifications to the packet structure specified by the standard. RAPID is implemented on an IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, which estimates the CIR from the reflections of transmitted packets. The proposed system is evaluated on a large dataset of CIR measurements, proving robustness across different environments and subjects, and outperforming state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching activity recognition and person identification accuracies of 94% and 90%, respectively.Comment: 16 pages, 18 figures, 4 table