Yunze Zeng

A first look at 802.11ac in action: Energy efficiency and interference characterization

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.

WiWho: wifi-based person identification in smart spaces

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 users 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 persons 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 persons 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 persons gait and identify the person. We discuss the potential and challenges of WiFi- based person identification with respect to smart space applications.

Analyzing Shopper's Behavior through WiFi Signals

Substantial progress in WiFi-based indoor localization has proven that pervasiveness of WiFi can be exploited beyond its traditional use of internet access to enable a variety of sensing applications. Understanding shoppers behavior through physical analytics can provide crucial insights to the business owner in terms of effectiveness of promotions, arrangement of products and efficiency of services. However, analyzing shoppers behavior and browsing patterns is challenging. Since video surveillance can not used due to high cost and privacy concerns, it is necessary to design novel techniques that can provide accurate and efficient view of shoppers behavior. In this work, we propose WiFi-based sensing of shoppers behavior in a retail store. Specifically, we show that various states of a shopper such as standing near the entrance to view a promotion or walking quickly to proceed towards the intended item can be accurately classified by profiling Channel State Information (CSI) of WiFi. We recognize a few representative states of shoppers behavior at the entrance and inside the store, and show how CSI-based profile can be used to detect that a shopper is in one of the states with very high accuracy (≈ 90%). We discuss the potential and limitations of CSI-based sensing of shoppers behavior and physical analytics in general.

Your AP knows how you move: fine-grained device motion recognition through WiFi

Recent WiFi standards use Channel State Information (CSI) feedback for better MIMO and rate adaptation. CSI provides detailed information about current channel conditions for different subcarriers and spatial streams. In this paper, we show that CSI feedback from a client to the AP can be used to recognize different fine-grained motions of the client. We find that CSI can not only identify if the client is in motion or not, but also classify different types of motions. To this end, we propose APsense, a framework that uses CSI to estimate the sensor patterns of the client. It is observed that clients sensor (e.g. accelerometer) values are correlated to CSI values available at the AP. We show that using simple machine learning classifiers, APsense can classify different motions with accuracy as high as 90%.

Monitoring vital signs using millimeter wave

Continuous monitoring of humans 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 humans 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.

PBA: Prediction-Based Authentication for Vehicle-to-Vehicle Communications

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 vehicles 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.

Sensor-Assisted Codebook-Based Beamforming for Mobility Management in 60 GHz WLANs

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.

Using Smartphone Sensors for Improving Energy Expenditure Estimation

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.

Vital Sign and Sleep Monitoring Using Millimeter Wave

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.

Accurate energy expenditure estimation using smartphone sensors

Accurate and online Energy Expenditure Estimation (EEE) utilizing small wearable sensors is a difficult task with most existing schemes. In this work, we focus on accurate EEE for tracking ambulatory activities of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, a generic regression model for EEE is built that yields upto 83% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band).