Oral diseases affect an estimated 3.5 billion people globally, posing significant health challenges. According to the World Health Organization (WHO), adopting self-care practices and maintaining personal oral hygiene can substantially mitigate the prevalence of dental caries. While smartwatches have previously been utilized to track activities of daily living (ADL), their widespread availability has yet to be harnessed for accurately identifying tooth brushing activity among other common ADL. In this Work in Progress (WIP), we demonstrate how motion sensors integrated into smartwatches can effectively distinguish tooth brushing from seven other very similar ADL. We present our initial results that show a promising 94% accuracy with 84% sensitivity.

Despite having numerous platforms to promote coronavirus awareness, a part of the population is not well informed about the basic knowledge related to the pandemic. This inspired us to design and implement a free-to-play game, Unlock Me , to help people learn about coronavirus easily yet effectively. A user-centric approach to designing the game has helped us understand the challenges people face and eventually to deliver an interactive game. We conducted an evaluation study across multiple age groups to understand the impact of Unlock Me to enhance COVID-19 learning of the player and to evaluate the quality of the game. The results are obtained by studying the player behavior and performing comparative analysis with Model for the Evaluation of Educational Games (MEEGA+), a standard game evaluation model. Our evaluation shows that there has been an increase in the awareness of players by 53% compared to pre-game awareness. 52.40% of the players found the game to be usable with a good player experience and learning.

This paper adopts a systems approach to study how millimeter wave (mmWave) radio transmitters on UAVs provide high throughput links under typical hovering conditions. With Terragraph channel sounder units, we experimentally study the impact of signal fluctuations and sub-optimal beam selection on a testbed involving DJI M600 UAVs. From the hovering-related insights and the measured antenna radiation patterns, we develop and validate the first stochastic UAV-to-Ground mmWave channel model with UAVs as transmitters. Our UAV-centric analytical model complements the classical fading with additional losses expected in the mmWave channel during hovering, considering 3-D antenna configuration and beamforming training parameters. We specifically consider lateral displacement, roll, pitch, and yaw, whose magnitude vary depending on the availability of specialized hardware such as real-time kinematic GPS. We then leverage this model to mitigate the hovering impact on the UAV-to-Ground link by selecting a near-to-optimum pair of beams. Importantly, our work does not change the wireless standard nor require any cross-layer information, making it compatible with current mmWave devices. Results demonstrate that our channel model drops estimation error to$\approx$0.2 percent, i.e., 18x lower, and improves the average PHY bit-rate by$\approx$10 percent when compared to existing state-of-the-art channel models and beamforming methods for UAVs.

The COVID-19 pandemic has presented unprecedented challenges across the world and universities are not saved either. Standard classroom activities in COVID era are even more challenging, with the primary challenge being ensuring physical distancing. We present a smart classroom system, BubbleNet, that attempts to relax these challenges. BubbleNet leverages cost-effective (~ $30) IoT nodes with motion sensors. The IoT nodes collaborate with each other via OpenThread - the latest open-source mesh networking protocol released by Google. In this paper, we present the development and demonstration of Bub-bieN et for monitoring physical distancing rules in a classroom.

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting `signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in `train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10-4for the evaluated cases.

5G and open radio access networks (Open RANs) will result in vendor-neutral hardware deployment that will require additional diligence towards managing security risks. This new paradigm will allow the same network infrastructure to support virtual network slices for transmit different waveforms, such as 5G New Radio, LTE, WiFi, at different times. In this multivendor, multi-protocol/waveform setting, we propose an additional physical layer authentication method that detects a specific emitter through a technique called as RF fingerprinting. Our deep learning approach uses convolutional neural networks augmented with triplet loss, where examples of similar/dissimilar signal samples are shown to the classifier over the training duration. We demonstrate the feasibility of RF fingerprinting base stations over the large-scale over-the-air experimental POWDER platform in Salt Lake City, Utah, USA. Using real world datasets, we show how our approach overcomes the challenges posed by changing channel conditions and protocol choices with 99.86% detection accuracy for different training and testing days.

Network densification through the deployment of WiFi access points (APs) is a promising solution towards achieving high connectivity rates required for emerging applications. A critical first step is to discover an AP before an active association between the client and the AP can be established. Legacy AP discovery procedures initiated by the client result in high latency in the order of a few 100 ms and waste spectrum, especially when clients need to frequently switch between multiple APs. We propose CSIscan that exploits the broadcast nature of WiFi channels by embedding discovery related information within an AP's ongoing regular transmissions. The AP does this by intelligently distorting the transmitted OFDM frame by inducing perturbations in the preamble, and these injected `bits' of information are detected via changes in the perceived channel state information (CSI). A deep learning framework allocates the optimal level of distortion on a per-subcarrier basis that keeps the resulting packet error rate to less than 1%. Existing clients perceive no changes in their ongoing communication, while potential new clients quickly obtain discovery information at the same time. We experimentally demonstrate that CSIscan reduces the overall WiFi latency from 150 ms to 10 ms and improves spectrum utilization with ~72% reduction in the probe traffic. We show that CSIscan delivers up to 40 discovery information bits in the outgoing WiFi packet in an indoor environment.

We address the problem of connecting IoT nodes to the Internet via challenged networks, which are characterized by minimal or intermittent network association. Examples of such networks can be found in forests with distant access points or in railway stations with overburdened access points. In such places, it is difficult for a node to establish and maintain an association with the access point. There exist several solutions that demonstrate the feasibility of data transfer using low bit-rate broadcast of discovery frames that do not necessitate association. We categorize them into a representative protocol called ViFi. However, the low bit-rate broadcast traffic hampers the performance of heavilyutilized networks by consuming significant airtime and resulting in a severe drop in goodput. We come up with a novel adaptive version of ViFi, Adaptive ViFi, that adapts to network conditions and intelligently selects a channel and an interval for the data transmission. In principle, our proposed solution is agnostic to WiFi standard, client, or AP in use. We develop an end-to-end working prototype on Raspberry Pi and hostapd-based access points. With a set of extensive experiments, we find that Adaptive ViFi gracefully addresses the limitations of ViFi in a heavily utilized network, at the same time it achieves up to 10.7x higher goodput than the ViFi. It co-exists well with other WiFi-based non-IoT clients with minimal impact on their goodput (4%). It scales well with a delivery rate of 77% and 50% with 150 and 300 nodes, respectively.

We address the problem of automating the process of network troubleshooting for a large-scale WiFi network. Specifically, we target identifying the causes of unnecessary active scans in WiFi networks, that are known to degrade the WiFi performance. We collect 340 hours worth of data with several thousands of episodes of active scans to train various machine learning models. Data is collected with 27 devices across vendors in varied network setups under a controlled setting. We study unsupervised and supervised machine learning techniques to conclude that a multilayer perceptron is the best model to detect the causes of active scanning. Further, we perform an in-vivo model validation in an uncontrolled real-world WiFi network. We also compare our models with a static rule-based approach. Our model improves the mean F1-score accuracy of cause detection from 0.78 to 0.99. Our proposed mechanism has the potential of being incorporated in the existing WiFi controllers, such as that of Cisco and Aruba.

Internet-of-Things (IoT) nodes in dense WiFi deployments struggle to maintain their associations with the access points (APs) and efficiently transmit their data. In the process, they waste their energy and airtime, which affects their battery-life and throughput. We present Elixir, a protocol for WiFi-based IoT nodes that enables them to transmit data without the need of a WiFi association. Elixir is implemented in the user space, which makes it easy to port the implementation to any Linux-based client and AP. We demonstrate its end-to-end operation on a Raspberry Pi module in good [signal strengths '40 to '50 dBm and no losses] and bad network conditions [signal strengths below '80 dBm and losses present]. We measure benefits of Elixir in terms of (a) the ability to transmit when the WiFi association cannot be maintained [in bad network condition], (b) lesser number of frames transmitted [50x - good network condition, 100x ? bad network condition], (c) reduced airtime consumption [10x - bad network condition], and (d) reduced battery expenditure [18% for both network conditions].

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps.

IEEE 802.11 WLAN (Wireless or WiFi LAN) clients discover neighboring APs (Access Points) by active or passive scanning. Such an active scan of WLAN injects probe frames in the network. Network conditions like packet losses, roaming, etc. result in increased active scanning and hence, an excessive increase of the probe traffic. Of the several causes inherent to WLANs like interference, we find an excessive probe traffic also has a potential of hampering goodput of a WiFi network. We confirm this behavior in a controlled home environment as well as in an uncontrolled enterprise environment. Our analysis of 36 hours of wireless traffic collected over a period of 5 months with approximately 45 million wireless frames reveals that goodput of a WLAN drops exponentially with increase in the probe traffic. Therefore, realtime detection of increase in probe traffic and knowledge of a threshold for acceptable probing is crucial for WLAN's performance. In this paper, we formulate a metric to measure the increase in probe traffic in realtime and evaluate its functioning empirically. The metric not only reflects increase in probe traffic correctly, it is even simple enough to allow its realtime measurement.