Millimeter-wave (mmWave) backscatter has emerged as a compelling low-power communication paradigm for high-bandwidth IoT applications. However, existing systems rely on specialized readers, such as FMCW radars, which limits their scalability and practical deployment. On the other hand, commodity mmWave backscatter integrates the tags directly into the mmWave networks with devices like APs and clients, and the protocol frames are retrofitted to enable seamless communication with the tags. Despite its potential, such bistatic backscatter communication suffers from low SNR and short communication range. In this work, we present B3, a bistatic backscatter beamforming framework that operates entirely on commodity mmWave infrastructure such as 802.11ad/ay. B3 introduces a lightweight multibeam backscatter technique that enables tags to embed both ID and channel information directly into standard beamforming frames via pulse position and on-off keying modulations. The design yields high-gain beams towards the tag in bistatic settings and supports concurrent multi-tag beamforming within a single training round. We prototype a multibeam 60 GHz backscatter tag and achieve a 13 dB SNR improvement compared to codebook-based beamforming, supporting backscatter communication at distances of up to 11 m with BER ≤ 10− 3. Our evaluation shows that B3 enables high-SNR backscatter even with blockages and NLoS conditions. Our tag prototype consumes only 2.5 mW of power, making it a practical and scalable solution.

Automotive FMCW radars remain reliable in rain and glare, yet their sparse, noisy point clouds constrain 3-D object detection. We therefore release CoVeRaP, a 21 k-frame cooperative dataset that time-aligns radar, camera, and GPS streams from multiple vehicles across diverse manoeuvres. Built on this data, we propose a unified cooperative-perception framework with middle- and late-fusion options. Its baseline network employs a multi-branch PointNet-style encoder enhanced with self-attention to fuse spatial, Doppler, and intensity cues into a common latent space, which a decoder converts into 3-D bounding boxes and per-point depth confidence. Experiments show that middle fusion with intensity encoding boosts mean Average Precision by up to 9 × at IoU 0.9 and consistently outperforms single-vehicle baselines. CoVeRaP thus establishes the first reproducible benchmark for multi-vehicle FMCW-radar perception and demonstrates that affordable radar sharing markedly improves detection robustness. Dataset and code are publicly available to encourage further research.

MmWave FMCW radars are integrated into many sensing systems for robust sensing. However, their sensing functions are vulnerable to spoofing attacks and interfered with by backscatter communications, both of which can cause sensor malfunction and system failure. Noticing that radar spoofing and communication share similar signal modulation mechanisms, in this paper, we present SCR, a new Spoofing-resilient and Communication-integrated Radar sensing scheme. SCR is based on the rigorous analysis of the radar sensing model that highlights the differences between modulated spoofing and communication signals and normal sensing signals reflected by natural objects. The key designs of SCR are a novel chirp configuration scheme and signal processing pipeline, which signify different patterns between modulated and normal signals in radar spectra, for reliable detection of spoofing and communication. We have developed SCR and tested it with actual 77 GHz mmWave radar sensors and backscatter prototypes. Our field tests show that SCR can reliably detect fake objects created by modulated signals in both velocity and distance radar sensing domains.

Occupancy sensing is essential for vehicle safety and security applications such as seat belt reminders, airbag deployment, intrusion detection, and child-left-behind alerts. This paper presents UMusic, a novel in-car occupancy sensing system that reuses the ultra-wideband (UWB) devices already installed for access control in modern vehicles. However, due to the compact size and metal structure, the in-car environment is full of reflected propagation paths, which cannot be precisely resolved even with UWB's wide-bandwidth feature. To overcome this challenge, UMusic introduces a reflected-path decomposition technique to extract a high-resolution power delay profile (PDP) from the channel impulse response (CIR) provided by commodity UWB devices, enabling precise environmental perception. By comparing PDPs in empty and occupied conditions, UMusic is able to detect the occupancy status in both a sedan and an SUV with multiple passengers across various scenarios. Our results show that UMusic achieves a 90.2% detection rate using a single CIR measurement collected within 50 ms, outperforming the state-of-the-art by 15.7%. When aggregating six consecutive CIR measurements, UMusic reaches 99.4% accuracy, demonstrating its effectiveness for real-world deployment.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we presentmilliEar, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio.milliEarcombines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluatemilliEarusing off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show thatmilliEarcan accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

As we move towards a future of connected and autonomous vehicles, high-speed and low-latency connectivity between vehicles is becoming increasingly important. This paper investigates enabling high data rate mmWave links in vehicle-to-vehicle (V2V) scenarios using street view images. We find that mmWave V2V links in urban settings suffer from frequent and prolonged blockages, resulting in unreliable connection and high beamforming overhead. Our work proposes mmSV, a system that creates 3D reflection profiles from street view images to assist vehicles in finding mmWave reflections from the environment in real-time. mmSV consists of two key components: material identification which identifies materials from street view images to determine their reflectivity and create 3D reflection map, and environment-driven ray-tracing and beamsearching which finds a high-SNR beam using predicted 3D material maps. Our extensive experimental results on the mmWave testbed show that mmSV can provide highly reliable V2V mmWave connectivity with low beamforming overhead.

Millimeter-wave (mmWave) communication is poised to significantly enhance vehicle-to-vehicle (V2V) networks by facilitating real-time data transmission between vehicles at gigabits-per-second (Gbps) data rates. However, the high relative mobility between vehicles results in substantial beamforming overhead, negatively affecting V2V network throughput and latency. In this paper, we introduce a novel real-time radarassisted beamforming approach for V2V networks and assess its performance in four typical scenarios using commercial off-the-shelf (COTS) devices. In the transmitter static scenario, our proposed scheme surpasses the default 802.11ad protocol by up to 54% in throughput. In the highly dynamic scenario, our approach yields a 67% improvement in throughput and exhibits 90% lower latency than the default 802.11ad protocol. Furthermore, we investigate a non-line-of-sight (NLOS) scenario, demonstrating that our proposed scheme can achieve higher data throughput rates by opting for the most robust beam sector rather than frequently alternating between weak beam patterns. Finally, the preliminary result in the highway scenario shows that our protocol can improve the throughput by 66% more than the default protocol.

As acoustic communication systems become more common in homes and offices, eavesdropping brings significant security and privacy risks. Current approaches of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words using classification, or cannot work through-wall due to the use of optical sensors. In this paper, we present MILLIEAR, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. MILLIEAR combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop with unconstrained vocabulary. We implement and evaluate MIL-LIEAR using off-the-shelf mmWave radar deployed in different scenarios and settings. We find that it can accurately reconstruct the audio even at different distances, angles and through the wall with different insulator materials. Our subjective and objective evaluations show that the reconstructed audio has a strong similarity with the original audio.

Massive connectivity is a key to the success of the Internet of Things. While mmWave backscatter has great potential, substantial signal attenuation and overwhelming ambient reflections impose significant challenges. We present OmniScatter, a practical mmWave backscatter with an extreme sensitivity of -115 dBm. The performance is theoretically comparable to the popular commodity RFID EPC Gen2 (900 MHz), and is empirically validated via evaluations under various practical settings with abundant ambient reflections and blockages - e.g., In an office where a tag is locked in a wooden closet 6m away, as well in libraries and retail stores where a tag is placed across two rows of metal shelves. At the heart of OmniScatter is the new High Definition FMCW (HD-FMCW), which interplays with the tag (FSK) signal to disentangle the ambient reflections from the tag signal in the frequency domain, essentially offering immunity to ambient reflections. To further support practical deployment, OmniScatter offers coordination-free Frequency Division Multiple Access (FDMA) that effortlessly scales to thousands of concurrent tags. The readers were built on commodity radars and the tags were prototyped on PCB. The trace-driven evaluation demonstrates concurrent communication of 1100 tags with the BER < 1.5%, paving a pathway towards practical mmWave backscatter for everyday and anywhere use.

With the explosive proliferation of wireless devices, our lives are improved by various applications supported by heterogeneous wireless technologies, such as WiFi and ZigBee. However, the coexistence of WiFi and ZigBee also results in the degradation of the network performance, which cannot be avoided if the WiFi devices are even unaware of the ambient ZigBee devices. To better accommodate the heterogeneous wireless devices, this paper presents X-Disco, the first cross-technology neighbor discovery mechanism, for a WiFi device to detect ZigBee neighbors, without modification to hardware or firmware. With the help of the recently proposed cross-technology communication, X-Disco enables a commodity WiFi device to trigger responses, containing ZigBee neighbor information, from the ambient ZigBee coordinators (including routers). Through exploring the WiFi PHY-layer information accessible by WiFi driver, X-Disco decodes the responded ZigBee messages and obtains the ZigBee neighbor information. To improve X-Disco's reliability, we also propose ZigBee neighbor validation and interruption mitigation to exclude hidden node terminals and mitigate the interference caused by the ambient WiFi traffic respectively. The evaluation of X-Disco is performed on the commodity devices (TP-Link WDR 4300 WiFi router, TelosB motes) and USRP B210. The results demonstrate X-Disco successfully detects nine ZigBee neighbors within 70ms in the office.

Multi-user volumetric content delivery can enable numerous appealing applications, such as online education, telehealth, multiuser AR/VR training, immersive collaborative analytics, etc. However, the bandwidth-intensive nature of volumetric video streaming makes existing systems for single-user experiences hard to scale to multi-user scenarios. To address this critical issue, in this paper, we first perform a scaling experiment on mmWave networks that offer the needed multi-Gbps throughput and identify two key challenges of streaming high-quality volumetric videos to multiple users: frequent blockages of mmWave links and high transmission redundancy among users. To solve these problems, we propose a first-of-its-kind, agile, and cross-layer system, dubbed M5, for improving the performance and quality of experience for multi-user volumetric video streaming. M5 utilizes the 6DoF motion prediction of users to proactively adapt mmWave beams and prefetch frames to mitigate the blockage effects. Furthermore, it takes advantage of the multicast transmission to deliver the overlapped common content within users' viewports to reduce the bandwidth requirement. Our extensive experiments on a real testbed and with a trace-driven simulator show that M5 can effectively improve the frame rate by 44.1% and volumetric video quality by 62.3% compared to the state-of-the-art system.

With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users’ privacy. While on smartphones the access to microphones needs an explicit user permission, acoustic eavesdropping attacks can rely on motion sensors (such as accelerometer and gyroscope), which access is unrestricted. However, previous instances of such attacks can only recognize a limited set of pre-trained words or phrases. In this paper, we present AccEar, an accelerometer-based acoustic eavesdropping attack that can reconstruct any audio played on the smartphone’s loudspeaker with unconstrained vocabulary. We show that an attacker can employ a conditional Generative Adversarial Network (cGAN) to reconstruct high-fidelity audio from low-frequency accelerometer signals. The presented cGAN model learns to recreate high-frequency components of the user’s voice from low-frequency accelerometer signals through spectrogram enhancement. We assess the feasibility and effectiveness of AccEar attack in a thorough set of experiments using audio from 16 public personalities. As shown by the results in both objective and subjective evaluations, AccEar successfully reconstructs user speeches from accelerometer signals in different scenarios including varying sampling rate, audio volume, device model, etc.

Although existing work has demonstrated the feasibility of streaming volumetric content to a single user, there exist many appealing applications (e.g., classroom education and collaborative design) that involve multiple users who watch the same volumetric content simultaneously. In this paper, we first perform a scaling experiment to demonstrate the challenges of streaming high-quality volumetric videos to multiple users and reveal the viewport-similarity opportunity that we can leverage to effectively optimize the network resource utilization using multicast over mmWave. We then develop a holistic research agenda for improving the performance and quality of experience for multi-user volumetric video streaming on commodity devices. Our proposed research includes joint viewport prediction and blockage mitigation for multiple users, multicast grouping based on viewport similarity, customized mmWave beam design for efficient multicast, and mmWave-aware multi-user video rate adaptation. Finally, we discuss the open challenges of building a practical system with the proposed research roadmap.

Gestures in American Sign Language (ASL) are characterized by fast, highly articulate motion of upper body, including arm movements with complex hand shapes and facial expressions. In this work, we propose a new method for word-level sign recognition from American Sign Language (ASL) using video. Our method uses both motion and hand shape cues while being robust to variations of execution. We exploit the knowledge of the body pose, estimated from an off-the-shelf pose estimator. Using the pose as a guide, we pool spatio-temporal feature maps from different layers of a 3D convolutional neural network. We train separate classifiers using pose guided pooled features from different resolutions and fuse their prediction scores during test time. This leads to a significant improvement in performance on the WLASL benchmark dataset [25]. The proposed approach achieves 10%, 12%, 9.5% and 6.5% performance gain on WLASL100, WLASL300, WLASL1000, WLASL2000 subsets respectively. To demonstrate the robustness of the pose guided pooling and proposed fusion mechanism, we also evaluate our method by fine tuning the model on another dataset. This yields 10% performance improvement for the proposed method using only 0.4% training data during fine tuning stage.

While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

In today's digital world, rapid technological advancements continue to lessen the burden of tasks for individuals. Among these tasks is communication across perceived language barriers. Indeed, increased attention has been drawn to American Sign Language (ASL) recognition in recent years. Camera-based and motion detection-based methods have been researched extensively; however, there remains a divide in communication between ASL users and non-users. Therefore, this research team proposes the use of a novel wireless sensor (Frequency-Modulated Continuous-Wave Radar) to help bridge the gap in communication. In short, this device sends out signals that detect the user's body positioning in space. These signals then reflect off the body and back to the sensor, developing thousands of cloud points per second, indicating where the body is positioned in space. These cloud points can then be examined for movement over multiple consecutive time frames using a cell division algorithm, ultimately showing how the body moves through space as it completes a single gesture or sentence. At the end of the project, 95% accuracy was achieved in one-object prediction as well as 80% accuracy on cross-object prediction with 30% other objects' data introduced on 19 commonly used gestures. There are 30 samples for each gesture per person from three persons.

This work presents an approach for American Sign Language (ASL) gesture recognition from videos. Gestures are comprised of various upper body motions involving hand shapes, motion of both hands with facial expression and head movements. Previous approaches tackled this problem by directly learning 3D convolutional spatio-temporal models from video in a simplified settings with uniform backgrounds. To handle more complex variation in appearance and backgrounds we propose to exploit recent advances in estimation of 2D body pose using Deep Convolutional Neural Networks trained on large corpus of human pose annotations. We use the trajectories of 2D skeletal data estimated from video to train a baseline recursive neural network gesture recognition model. The basic model is further extended using embeddings of hand images obtained from another hand shape recognition model [15] with dynamics modeled by another recursive neural network. The final model learns how to fuse two Long Short Term Model (LSTM) recursive neural network models for skeletal and hand image data. We train and evaluate this model on the GMU-ASL51 dataset of 12 users and 51 ASL gestures [8] demonstrating its superior performance compared to several baseline models.

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures. These are comprised of arm movements with different hand shapes, facial expression and head movements. Among these components, hand shape is the vital, often the most discriminative part of a gesture. In this work, we present an approach for effective learning of hand shape embeddings, which are discriminative for ASL gestures. For hand shape recognition our method uses a mix of manually labelled hand shapes and high confidence predictions to train deep convolutional neural network (CNN). The sequential gesture component is captured by recursive neural network (RNN) trained on the embeddings learned in the first stage. We will demonstrate that higher quality hand shape models can significantly improve the accuracy of final video gesture classification in challenging conditions with variety of speakers, different illumination and significant motion blurr. We compare our model to alternative approaches exploiting different modalities and representations of the data and show improved video gesture recognition accuracy on GMU-ASL51 benchmark dataset.

Over half a million people in the United States use American Sign Language (ASL) as their primary mode of communication. Automatic ASL recognition would enable Deaf and Hard of Hearing (DHH) users to interact with others who are not familiar with ASL as well as voice-controlled digital assistants (e.g., Alexa, Siri, etc.). While ASL recognition has been extensively studied, there is a little attention given to recognition of ASL non-manual body markers. The non-manual markers are typically expressed through head, torso and shoulder movements, and add essential meaning and context to the signed sentences. In this work, we present ExASL, a sentence-level ASL recognition system using millimeter-wave radars. ExASL can recognize manual markers (hand gestures) and non-manual markers (head and torso movements). It utilizes multi-distance clustering to recognize body parts and cluster mmWave point clouds. We then present a multi-view deep learning algorithm that can learn from clustered body part representation for an expressive sentence-level recognition. Our evaluation shows that ExASL can recognize ASL sentences with a word error rate of 0.79%, sentence error rate of 1.25%, and non-manual markers with an accuracy of 83.5%.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses, etc. Such communication has a unique property that a user can visually identify a transmitter (i.e., LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement, etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 7.7 Kbps on Nexus 5, 3.7 Kbps on iPhone 5S, and 2.9 Kbps on Samsung Note8. It is also shown that lower CSK modulations (e.g., four and eight CSK) provide extremely low symbol error rates (-3), making them a desirable choice for reliable LED-to-camera communication.

Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-of-Hearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a large-scale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMU-ASL51. We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants.

Dense deployment of access points in 60 GHz WLANs can provide always-on gigabit connectivity and robustness against blockages to mobile clients. However, this dense deployment can lead to harmful interference between the links, affecting link data rates. In this paper, we attempt to better understand the interference characteristics and effectiveness of interference mitigation techniques using 802.11ad COTS devices and 60 GHz software radio based measurements. We first find that current 802.11ad COTS devices do not consider interference in sector selection, resulting in high interference and low spatial reuse. We consider three techniques of interference mitigation - channelization, sector selection and receive beamforming. First, our results show that channelization is effective but 60 GHz channels have non-negligible adjacent and non-adjacent channel interference. Second, we show that it is possible to perform interference-aware sector selection to reduce interference but its gains can be limited in indoor environment with reflections, and such sector selection should consider fairness in medium access and avoid asymmetric interference. Third, we characterize the efficacy of receive beamforming in combating interference and quantify the related overhead involved in the search for receive sector, especially in presence of blockages. We elaborate on the insights gained through the characterization and point out important outstanding problems through the study.

The 3rd ACM Workshop on Millimeter-Wave Wireless Networks and Sensing Systems (mmNets'19) is focused on the design and implementation of new millimeter-wave (mmWave) protocols and systems that can enable multi-Gbps wireless connectivity for 5G-and-beyond cellular systems and wireless LANs, as well as new wireless sensing and imaging systems. The goal of the mmNets'19 workshop is to bring together researchers from mmWave hardware, communication and signal processing, wireless networking, and mobile applications to set the future research agenda of mmWave systems, and present innovative ideas that will help realize the vision of extremely high data rate wireless networks and novel advanced sensing applications. The workshop will serve as a platform for both academia and industry to identify key challenges, present solutions and advance the field of mmWave technology.

60 GHz millimeter-wave networks have emerged as a potential candidate for designing the next generation of multi-gigabit WLANs. Since the 60 GHz links suffer from frequent outages due to blockages caused by human mobility, deploying 60 GHz WLANs that can provide robust coverage in presence of blockages is a challenging problem. In this paper, we study blockage-aware coverage and deployment of 60 GHz WLANs. We first show that the reflection profile of an indoor environment can be sensed using a few measurements. A novel coverage metric (angular spread coverage) which captures the number of available paths and their spatial diversity is proposed. Additionally, it is shown that using relays can extend the coverage of the AP at a lower cost and provide added spatial diversity in the available paths. We propose a heuristic algorithm that determines the AP and relay locations while maximizing the angular spread coverage metric for the clients. Our testbed-based evaluation shows that for five different rooms, our proposed deployment can guarantee an average connectivity of 91.7%, 83.9%, and 74.1% of client locations in the presence of 1, 3 and 5 concurrent human blockages respectively, substantially increasing the robustness of 60 GHz links against blockages.

60 GHz millimeter-wave WLANs are gaining traction with their ability to provide multi-gigabit per second data rates. In spite of their potential, link outages due to human body blockage remain a challenging outstanding problem. In this work, we propose mmChoir, a novel proactive blockage mitigation technique that utilizes joint transmissions from multiple Access Points (APs) to provide blockage resilience to clients. We derive a new reliability metric based on angular spread of incoming paths to a client and their blockage probabilities. The metric can be used to intelligently select joint transmissions that can provide higher reliability. The reliability metric along with a novel interference estimation model, is used by mmChoir's scheduler to judiciously schedule joint transmissions, and increase network capacity and reliability. Our testbed and trace-driven simulations show that mmChoir can outperform existing beamswitching based blockage mitigation scheme with on an average 58% higher network throughput.

User's personal portable devices such as smartphone, tablet and laptop require continuous authentication of the user to prevent against illegitimate access to the device and personal data. Current authentication techniques require users to enter password or scan fingerprint, making frequent access to the devices inconvenient. In this work, we propose to exploit user's on-body wearable devices to detect their proximity from her portable devices, and use the proximity for continuous authentication of the portable devices. We present PCASA which utilizes acoustic communication for secure proximity estimation with sub-meter level accuracy. PCASA uses Differential Pulse Position Modulation scheme that modulates data through varying the silence period between acoustic pulses to ensure energy efficiency even when authentication operation is being performed once every second. It yields an secure and accurate distance estimation even when user is mobile by utilizing Doppler effect for mobility speed estimation. We evaluate PCASA using smartphone and smartwatches, and show that it supports up to 34 hours of continuous authentication with a fully charged battery.

Estimation of building occupancy has emerged as an important research problem with applications ranging from building energy efficiency, control and automation, safety, communication network resource allocation, etc. In this research work, we propose the estimation of occupancy using non-intrusive information that is already available from existing sensing modes, namely, number of WiFi devices, electrical energy demand and water consumption rate. Using data collected from 76 buildings in a university campus, we study the feasibility of multi-modal fusion between the three data sources for estimating fine-grained occupancy. In order to make the estimation model scalable, we propose three different clustering schemes to identify similarity in building characteristics and training per-cluster occupancy estimation models. The presented multi-modal fusion estimation framework achieves a mean absolute percentage error of 13.22% and we find that leveraging all three modalities provide an improvement of 48% in accuracy as compared to WiFi-only occupancy estimation. Our evaluation also shows that clustering buildings greatly increases the scalability of the proposed approach through significant reduction in training overhead, while providing an accuracy comparable to exhaustive, per-building estimation models.

Continuous monitoring of human’s breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This article presents mmVital, a system that uses 60GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human’s body and the Received Signal Strength (RSS) of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital also provides sleep monitoring with sleeping posture identification and detection of central apnea and hypopnea events. It relies on a novel human finding procedure where a human can be located within a room by reflection loss-based object/human classification. We evaluate mmVital using a 60GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 breaths per minute (Bpm; breathing rate) and 2.15 beats per minute (bpm; heart rate). Also, it can locate the human subject with 98.4% accuracy within 100ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind a wall.

There has been a growing interest in equipping the objects and environment surrounding the user with sensing capabilities. Smart indoor spaces such as smart homes and offices can implement the sensing and processing functionality, relieving users from the need of wearing or carrying smart devices. Enabling such smart spaces requires device-free effortless sensing of user's identity and activities. Device-free sensing using WiFi has shown great potential in such scenarios, however, fundamental questions such as person identification have remained unsolved. In this paper, we present WiWho, a framework that can identify a person from a small group of people in a device-free manner using WiFi. We show that Channel State Information (CSI) used in recent WiFi can identify a person's steps and walking gait. The walking gait being distinguishing characteristics for different people, WiWho uses CSI-based gait for person identification. We demonstrate how step and walk analysis can be used to identify a person's walking gait from CSI, and how this information can be used to identify a person. WiWho does not require a person to carry any device and is effortless since it only requires the person to walk for a few steps (e.g. entering a home or an office). We evaluate WiWho using experiments at multiple locations with a total of 20 volunteers, and show that it can identify a person with average accuracy of 92% to 80% from a group of 2 to 6 people. We also show that in most cases walking as few as 2-3 meters is sufficient to recognize a person's gait and identify the person. We discuss the potential and challenges of WiFi- based person identification with respect to smart space applications.

We propose human mobility tracking and activity monitoring using 60 GHz millimeter wave (mmWave). We discuss the benefits of using mmWave signals for the purpose over existing 2.4/5 GHz based techniques. We also identify related challenges of determining human's initial location and tracking, and demonstrate the feasibility of activity monitoring using an example of walking activity.

Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of- concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

The number of wireless-enabled devices owned by a user has had huge growth over the past few years. Over one third of adults in the United States currently own three wireless devices: a smartphone, laptop, and tablet. This article provides a study of the network usage behavior of today’s multidevice users. Using data collected from a large university campus, we provide a detailed multidevice user (MDU) measurement study of more than 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affects the network usage behavior of users. Specifically, we characterize the usage pattern of the different device types in terms of total and intermittent usage, how the usage of different devices overlap over time, and uncarried device usage statistics. We also study user preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about MDUs. We see how the use of tablets and laptops are interchangeable and how the overall multidevice usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices, which results in inefficient utilization of available IP address space. All findings about multidevice usage patterns have the potential to be utilized by different entities, such as app developers, network providers, security researchers, and analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two devices among a smartphone, tablet, and laptop.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (≈ 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (< 10--3), making them a desirable choice for reliable LED-to-camera communication.

Building security systems are commonly deployed to detect intrusion and burglary in home and business structures. Such systems can accurately detect door open/close events, but their high-cost of installation and maintenance makes them unsuitable for certain building monitoring applications, such as times of high/low entrance traffic, estimating building occupancy, etc. In this paper, we show that barometer sensors found in latest smartphones can directly detect the building door open/close events anywhere inside an insulated building. The sudden pressure change observed by barometers is sufficient to detect events even in presence of user mobility (e.g. climbing stairs). We study various characteristics of the pressure variation due to door events, and demonstrate that door open/close events can be recognized with an accuracy range of 99.34% -- 99.81% based on the data collected from 3 different buildings. Such a low-cost ubiquitous solution of door event detection enables many monitoring applications without any infrastructure integration, and it can also work as an augmentation to the existing expensive security systems.

Mobile advertisements have become the dominant source of revenue for mobile application developers, advertisers and brokers. Using novel sensing techniques and the advanced sensors of mobile devices, it has become feasible to determine a user's fine-grained context such as her location, activity, and interests. This information can be used by the advertisement (ad) brokers to provide more relevant ads to the user based on her context. However, this has led to serious privacy risks, since a user can be tracked by the broker or an adversary based on her context. In this paper, we present AnonAd, an ad delivery scheme that allows users to protect their privacy when receiving micro-targeted ads from the broker. AnonAd utilizes the encryption of the user's context based on a split-secret scheme that guarantees that the broker can decrypt the context only when there exists k other users in the same context. This way, a user's privacy is protected with k-anonymity during the context report. We show that the split-secret scheme integrates seamlessly with existing homomorphic encryption-based schemes that can provide differential privacy for ad click reports. We implement AnonAd on Android smartphones and evaluate it with real users as well as simulated users that follow real mobility traces. Our results show that AnonAd achieves a balance between user's privacy and relevancy of advertisements without the requirement of any additional proxy servers.

Smartphones are closely coupled with their users and smartphone sensors can perceive users' private information. The existing studies in this area focus on user activity recognition and short-term context detection. In this paper, we show that smartphone sensors are able to profile users' long-term privacy and more sensitive information. We present the techniques of discovering users' spending level by merely using smartphone sensors. We do not access users' contacts, calendar, or call log, so that the profiling is performed in a non-intrusive manner. This paper is an alert towards the public that the privacy leakage could be far worse than imagination by just carrying smartphones.

The potential to provide multi-gbps throughput has made 60 GHz communication an attractive choice for next-generation WLANs. Due to highly directional nature of the communication, a 60 GHz link faces frequent outages in the presence of mobility. In this work, we present a sensor-assisted multi-level codebook-based beam width adaptation and beam switching to address the mobility challenges in 60 GHz WLANs. First, we show that by combining antenna element selection with codebook design, it is possible to generate a multilevel codebook that can cover different beam forming directions with many possible beam widths and directive gain. Second, we propose that accelerometer and magnetometer sensors which are commonly available on mobile devices can be used to better account for mobility, and perform near-real time beam width adaptation and beam switching. We evaluate the sensor-assisted multi-level codebook-based beam forming with trace-driven simulations using real mobility traces. Numeric evaluation shows that such beam forming can maintain the connectivity over 84% of the time even in presence of high device mobility.

Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user?s voice input through the microphone. Such applications utilize hotwords such as "Okay Google" or "Hi Galaxy" allowing them to distinguish user?s voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices.

No abstract available.

There has been a huge growth in the number of wireless-enabled devices possessed by a user. Over two third of adults in United States currently own three devices - laptop, smartphone and tablet. In this paper, we provide a first look at the network usage behavior of today's multi-device users. Using the data collected from a large university campus, we provide a detailed measurement-based characterization study of over 30,000 users. Our objective is to understand how existence of multiple wireless devices affect the network usage behavior of users. Specifically, we study the usage pattern of devices, how the usage of difference devices overlap in time, user's preferences of accessing sensitive content and device-specific factors that govern their choice of WiFi encryption type. The study reveals numerous interesting findings such as how current DHCP configurations are oblivious to multiple devices which results in inefficient utilization of available IP address space.

Wearable smart devices such as smart-glasses, smart-watches and life-logging devices are becoming increasingly popular, and majority of them are being equipped with first-person cameras. Such first-person cameras on smart-glasses or lifeloggers capture photos/videos from user's point of view, allowing them to record and share user's everyday events. However, these wearable devices with first-person cameras raise serious privacy concerns because they can also capture extremely private moments and sensitive information of the user. Currently, such devices lack the intelligence to understand user's preferences about certain scenarios being sensitive/private. To address this problem, we present PriFir, a scheme that enables Privacy-preserving First-person cameras. PriFir is based on the idea that low-power sensors (e.g. accelerometer, light sensor, etc.) embedded in smartphones and smart-watches can be leveraged to identify sensitive scenarios. Learning from user's preferences, PriFir employs a cascade of classifiers that tags a scenario to be sensitive simply based on the characteristics of the low-power sensor data. We evaluate PriFir using real sensor traces spanning over multiple days and show that it performs highly accurate classification at a low energy cost.

It is understood from past decade of research that a wireless multi-hop network can achieve maximum network throughput only when its nodes operate at a minimum common transmission power level that ensures network connectivity (availability). This point of optimality where maximum availability and throughput is guaranteed in an interference-optimal network has been the basis of numerous design problems in wireless networks. In this paper, we claim that when performability (availability weighted performance) is considered as opposed to average case throughput performance, there does not exist a transmission power (or node density) that can maximize both availability and performability. Since the current mesh networks are expected to deliver carrier-grade services to its users, the availability-performability tradeoff presented in this paper holds a special importance. While availability metric is a necessary one for any networking system intended to provide continuous service, past research has shown a strong correlation between performability and quality of user experience in case of wireless networks. The contributions of the paper are as follows: (1) We first define availability and performability in the context of wireless mesh networks, and then develop efficient algorithms on the basis of intelligent state sampling that can calculate both the quantities with reasonable accuracy. (2) We apply the evaluation methods to two existing mesh networks (GoogleWiFi and PoncaCityMesh) to demonstrate that their current design can not guarantee a reasonable level of availability or performability. (3) Using hundreds of hours of simulations, we analyze the impact of two basic deployment factors (node density and transmission power) on availability and performability. We outline numerous novel results that emerge due to joint availability-performability analysis including the observation about availability-performability tradeoff.

The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery based on user's location. Since sharing precise location remains a major privacy concern among the users, many location-based services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100 gigabytes of total data. Our results show that PACL (built using decision tree) can predict user's contextual location with the accuracy of around 87%.

This paper is first of its kind in presenting a detailed characterization of IEEE 802.11ac using real experiments. 802.11ac is the latest WLAN standard that is rapidly being adapted due to its potential to deliver very high throughput. The throughput increase in 802.11ac can be attributed to three factors — larger channel width (80/160 MHz), support for denser modulation (256 QAM) and increased number of spatial streams for MIMO. We provide an experiment evaluation of these factors and their impact using a 18-nodes 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice. Since utilization of larger channel width is one of the most significant changes in 802.11ac, we focus our study on understanding its impact on energy efficiency and interference. Using experiments, we show that utilizing larger channel width is in general less energy efficient due to its higher power consumption in idle listening mode. Increasing the number of MIMO spatial streams is comparatively more energy efficient for achieving the same percentage increase in throughput. We also show that 802.11ac link witnesses severe unfairness issues when it coexists with legacy 802.11. We provide a detailed analysis to show how medium access in heterogeneous channel width environment leads to the unfairness issues. We believe that these and many other findings presented in this work will help in understanding and resolving various performance issues of next generation WLANs.

With recent advances in Software-defined Radios (SDRs), it has indeed became feasible to dynamically adapt the channel widths at smaller time scales. Even though the advantages of varying channel width (e.g. higher link throughput with higher width) have been explored before, as with most of the physical layer settings (rate, transmission power etc.), naively configuring channel widths of links can in fact have negative impact on wireless network performance. In this paper, we design a cross-layer channel width assignment scheme that adapts the width according to the backlog of link-layer queues. We leverage the benefits of varying channel widths while adhering to the invariants of back-pressure utility maximization framework. The presented scheme not only guarantees improved throughput and network utilization but also ensures bounded buffer occupancy and fairness.

The back-pressure based scheduling policy originally proposed by Tassiulas et al. in [1] has shown the potential of solving many fairness and network utilization related problems of wireless multi-hop networks. Recently, the scheduling policy has been adapted in random medium access protocols such as CSMA/CA using prioritization of MAC layer transmissions. Here, MAC priorities are used to provide differentiated services to nodes depending on their queue backlogs. Even though these schemes work well in experiments to emulate back-pressure scheduling, they perform poorly with realistic Internet-type traffic where there is a large variation in packet sizes. In this paper, we propose packet aggregation based back-pressure scheduling which aggressively increases the rates at which back-logged queues are served. Different from other aggregation schemes, the presented scheme utilizes the back-pressure principles for determining when and how much aggregation is performed. We show that this results into increased service rates of back-logged queues which in turn results into high network throughput and utilization. We verify our scheme using simulations and testbed experiments, and show that it achieves significant performance improvements as compared to the original scheme.

In this paper, we consider power control as network layer problem in wireless mesh networks. The network connectivity between nodes is determined by their communication range which in turn can be controlled by adjusting the transmit power level. It is generally acknowledged that reducing transmit power levels of nodes to the minimum required to retain connectivity always increases network capacity. In this work, we show that though this is true for CSMA/CA based medium access, increasing power level of nodes can be beneficial in many cases when links are TDM-scheduled. Based on analysis and simulations, it is observed that increasing power levels of nodes (and decreasing number of hops in routing paths) results in increase of throughput in many representative traffic patterns and topologies. We characterize achievable spatial reuse and capacity with respect to power control in different topologies and traffic patterns. With increasing number of MAC protocols adopting TDMA approach, results presented here can be crucial in understanding how capacity is affected with varying levels of network connectivity.

When shortest path routing is employed in large scale multi-hop wireless networks, nodes located near the center of the network have to perform disproportionate amount of relaying for others. To solve the problem, various divergent routing schemes are used which route the data on center-avoiding divergent routing paths. Though they achieve better load balancing, overall relaying is increased significantly due to their longer routing paths. In this paper, we propose power control as a way for balancing relay load and mitigating hot-spots in wireless networks. Using a heuristic based on the concept of centrality, we show that if we increase the power levels of only the nodes which are expected to relay more packets, significant relay load balancing can be achieved even with shortest path routing. Different from divergent routing schemes, such load balancing strategy is applicable to any arbitrary topology. Also, it is shown that centrality based power control results into better throughput capacity in many different topologies.

When shortest path routing is employed in large scale multi-hop wireless networks, nodes located near the center of the network have to perform disproportional amount of relaying for others. In energy-constrained networks like sensor, such unfair forwarding results into early depletion of batteries of these congested nodes. To solve the problem, various divergent routing schemes are used which route the data on center-avoiding divergent routing paths. Though they achieve better load balancing, overall relaying is increased significantly due to their longer routing paths which in turn results into reduced energy efficiency. In this paper, we propose power control as a way of achieving better load balancing in multi-hop wireless networks. We show that when communication range of nodes are properly controlled using power control, better load balancing can be achieved using shortest paths only. Such a strategy also decreases overall relaying in the network when compared to divergent routing schemes. We use the concept of centrality to achieve appropriate balance between relay burden of nodes and their power levels. Numerical results confirm that centrality based load balancing significantly improves network lifetime of sensor networks.