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.

Automobile manufacturers are building cars with passive keyless entry support – the ability for users to unlock and start their cars while having the car keys "in their pockets". In this regard, car manufacturers are currently installing Ultra-Wideband (UWB) radios in cars owing to their robustness to relay attacks and their precise ranging estimates. In this paper, we explore the possibility of using these UWB radios (that exist for keyless entry) as an orthogonal sensing modality. To test this hypothesis, we build CaraoKey, the first system that exploits the UWB keyless infrastructure as a "sensor". More specifically, CaraoKey leverages the UWB radios to infer the state of a car (empty, door, window or trunk open, person inside the car, etc.). It does so by building a multipath profile based on the Channel Impulse Response (CIR) that is computed by each UWB node. We design and implement a 14-node system (a superset of all possible node locations) and evaluate it at 7 different indoor and outdoor locations while capturing a wide range of factors such as cars, people, walls on the sides, etc. Our results indicate that CaraoKey can distinguish 8 different car states with 98% accuracy using 8 nodes, and nearly 94% accuracy using just 4 nodes.

Major Wi-Fi Access Point (AP) vendors worldwide seek to provide gigabit wireless connectivity, by densely deploying MU-MIMO capable APs, which can support multiple, concurrent data streams to a group of clients, connected to them. However, MU-MIMO gains can only be achieved if an AP can identify groups of clients with homogenous configurations and orthogonal wireless channels, where concurrent transmissions will not cause inter-client interference. Hence, MU-MIMO performance is fundamentally depending on how the clients are assigned to APs. Our experiments with 802.11ac commodity testbeds show that state-of-the-art client assignment algorithms are MU-MIMO oblivious and limit the MU-MIMO grouping opportunities in realistic settings. In this paper, we design and implement MAPS, an MU-MIMO-Aware AP Selection algorithm that is 802.11-compliant and can boost network's MU-MIMO throughput gains. We verified MAPS' gains over legacy designs via extensive experiments with 802.11ac commodity testbeds.

Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.

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.

Privacy and authenticity of data pushed by or into wearable devices is of important concern. We investigate the possibility of an implicit authentication approach for wearable devices based on ambulatory activities performed by the user. Our initial results show that the proposed approach can achieve as high as 97% accuracy to authenticate a user.

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.

In vehicular networks, broadcast communications are critically important, as many safety-related applications rely on single-hop beacon messages broadcast to neighbor vehicles. However, it becomes a challenging problem to design a broadcast authentication scheme for secure vehicle-to-vehicle communications. Especially when a large number of beacons arrive in a short time, vehicles are vulnerable to computation-based Denial of Service (DoS) attacks that excessive signature verification exhausts their computational resources. In this paper, we propose an efficient broadcast authentication scheme called Prediction-Based Authentication (PBA) to not only defend against computation-based DoS attacks, but also resist packet losses caused by high mobility of vehicles. In contrast to most existing authentication schemes, our PBA is an efficient and lightweight scheme since it is primarily built on symmetric cryptography. To further reduce the verification delay for some emergency applications, PBA is designed to exploit the sender vehicle’s ability to predict future beacons in advance. In addition, to prevent memory-based DoS attacks, PBA only stores shortened re-keyed Message Authentication Codes (MACs) of signatures without decreasing security. We analyze the security of our scheme and simulate PBA under varying vehicular network scenarios. The results demonstrate that PBA fast verifies almost 99 percent messages with low storage cost not only in high-density traffic environments but also in lossy wireless environments.

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.

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.