Bio: Xin Qi is an academic researcher from College of William & Mary. The author has contributed to research in topics: Wireless sensor network & Energy consumption. The author has an hindex of 11, co-authored 24 publications receiving 481 citations. Previous affiliations of Xin Qi include Shenyang Institute of Automation & VMware.
TL;DR: This paper proposes a continuous and noninvasive authentication system for wearable glasses, named GlassGuard, which discriminates the owner and an impostor with behavioral biometrics from six types of touch gestures and voice commands, which are all available during normal user interactions.
Abstract: Wearable glasses are on the rising edge of development with great user popularity. However, user data stored on these devices bring privacy risks to the owner. To better protect the owner's privacy, a continuous authentication system is needed. In this paper, we propose a continuous and noninvasive authentication system for wearable glasses, named GlassGuard. GlassGuard discriminates the owner and an impostor with behavioral biometrics from six types of touch gestures (single-tap, swipe forward, swipe backward, swipe down, two-finger swipe forward, and two-finger swipe backward) and voice commands, which are all available during normal user interactions. With data collected from 32 users on Google Glass, we show that GlassGuard achieves 99% detection rate and 0.5% false alarm rate after 3.5 user events on average when all types of user events are available with equal probability. Under five typical usage scenarios, the system has a detection rate above 93% and a false alarm rate below 3% after less than five user events.
••09 Apr 2013
TL;DR: AdaSense is a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement and outperforms a state-of-the-art solution in terms of energy savings.
Abstract: In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.
••05 Sep 2012
TL;DR: SAPSM is proposed: Smart Adaptive Power Save Mode that labels each application with a priority with the assistance of a machine learning classifier and improves energy savings by up to 56% under typical usage patterns.
Abstract: Effective WiFi power management can strongly impact the energy consumption on Smartphones. Through controlled experiments, we find that WiFi power management on a wide variety of Smartphones is a largely autonomous process that is processed completely at the driver level. Driver level implementations suffer from the limitation that important power management decisions can be made only by observing packets at the MAC layer. This approach has the unfortunate side effect that each application has equal opportunity to impact WiFi power management to consume more energy, since distinguishing between applications is not feasible at the MAC layer. The power cost difference between WiFi power modes is high (a factor of 20 times when idle), therefore determining which applications are permitted to impact WiFi power management is an important and relevant problem. In this paper we propose SAPSM: Smart Adaptive Power Save Mode. SAPSM labels each application with a priority with the assistance of a machine learning classifier. Only high priority applications affect the client's behavior to switch to CAM or Active mode, while low priority traffic is optimized for energy efficiency. Our implementation on an Android Smartphone improves energy savings by up to 56% under typical usage patterns.
TL;DR: This paper considers a two-hop data communication system composed of a body sensor network (BSN) and a WiFi network and formulates an energy consumption optimization problem with the constraints of both throughput and time delay, and converts this problem into a geometric programming problem, which is numerically solved.
Abstract: In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network.
••18 May 2015
TL;DR: This paper conducts the first large-scale measurement study on the Android I/O delay using the data collected from the Android application running on 2611 devices within nine months, and observes that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads.
Abstract: The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this paper, we investigate the behavior of reads and writes in smartphones. We conduct the first large-scale measurement study on the Android I/O delay using the data collected from our Android application running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. Additionally, we show the asymmetry of the slowdown of one I/O type due to another, and elaborate the speedup of concurrent I/Os over serial ones. We use this obtained knowledge to design and implement a system prototype called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO issues I/Os with optimized concurrency parameters. The system is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
TL;DR: The emerging researches of deep learning models for big data feature learning are reviewed and the remaining challenges of big data deep learning are pointed out and the future topics are discussed.
Abstract: Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and pattern recognition. Recent development in sensor networks and communication technologies has enabled the collection of big data. Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity. In the past few years, deep learning has played an important role in big data analytic solutions. In this paper, we review the emerging researches of deep learning models for big data feature learning. Furthermore, we point out the remaining challenges of big data deep learning and discuss the future topics.
TL;DR: In this article, Stann et al. present RMST (Reliable Multi-Segment Transport), a new transport layer for Directed Diffusion, which provides guaranteed delivery and fragmentation/reassembly for applications that require them.
Abstract: Appearing in 1st IEEE International Workshop on Sensor Net Protocols and Applications (SNPA). Anchorage, Alaska, USA. May 11, 2003. RMST: Reliable Data Transport in Sensor Networks Fred Stann, John Heidemann Abstract – Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair. Second, these energy constraints, plus relatively low wireless bandwidths, make in-network processing both feasible and desirable . Third, because nodes in sensor networks are usually collaborating towards a common task, rather than representing independent users, optimization of the shared network focuses on throughput rather than fairness. Finally, because sensor networks are often deployed by a single organization with inexpensive hardware, there is less need for interoperability with existing standards. For all of these reasons, sensor networks provide an environment that encourages rethinking the structure of traditional communications protocols. The main contribution is an evaluation of the placement of reliability for data transport at different levels of the protocol stack. We consider implementing reliability in the MAC, transport layer, application, and combinations of these. We conclude that reliability is important at the MAC layer and the transport layer. MAC-level reliability is important not just to provide hop-by-hop error recovery for the transport layer, but also because it is needed for route discovery and maintenance. (This conclusion differs from previous studies in reliability for sensor nets that did not simulate routing. ) Second, we have developed RMST (Reliable Multi-Segment Transport), a new transport layer, in order to understand the role of in- network processing for reliable data transfer. RMST benefits from diffusion routing, adding minimal additional control traffic. RMST guarantees delivery, even when multiple hops exhibit very high error rates. 1 Introduction Wireless sensor networks provide an economical, fully distributed, sensing and computing solution for environments where conventional networks are impractical. This paper explores the design decisions related to providing reliable data transport in sensor nets. The reliable data transport problem in sensor nets is multi-faceted. The emphasis on energy conservation in sensor nets implies that poor paths should not be artificially bolstered via mechanisms such as MAC layer ARQ during route discovery and path selection . Path maintenance, on the other hand, benefits from well- engineered recovery either at the MAC layer or the transport layer, or both. Recovery should not be costly however, since many applications in sensor nets are impervious to occasional packet loss, relying on the regular delivery of coarse-grained event descriptions. Other applications require loss detection and repair. These aspects of reliable data transport include the provision of guaranteed delivery and fragmentation/ reassembly of data entities larger than the network MTU. Sensor networks have different constraints than traditional wired nets. First, energy constraints are paramount in sensor networks since nodes can often not be recharged, so any wasted energy shortens their useful lifetime . This work was supported by DARPA under grant DABT63-99-1-0011 as part of the SCAADS project, and was also made possible in part due to support from Intel Corporation and Xerox Corporation. Fred Stann and John Heidemann are with USC/Information Sciences Institute, 4676 Admiralty Way, Marina Del Rey, CA, USA E-mail: firstname.lastname@example.org, email@example.com. 2 Architectural Choices There are a number of key areas to consider when engineering reliability for sensor nets. Many current sensor networks exhibit high loss rates compared to wired networks (2% to 30% to immediate neighbors)[1,5,6]. While error detection and correction at the physical layer are important, approaches at the MAC layer and higher adapt well to the very wide range of loss rates seen in sensor networks and are the focus of this paper. MAC layer protocols can ameliorate PHY layer unreliability, and transport layers can guarantee delivery. An important question for this paper is the trade off between implementation of reliability at the MAC layer (i.e. hop to hop) vs. the Transport layer, which has traditionally been concerned with end-to-end reliability. Because sensor net applications are distributed, we also considered implementing reliability at the application layer. Our goal is to minimize the cost of repair in terms of transmission.
TL;DR: The communication security issues facing the popular wearables is examined followed by a survey of solutions studied in the literature, and the techniques for improving the power efficiency of wearables are explained.
Abstract: As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices—wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accessories and clothing that people wear. Although small in size, they are often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put significant demand on improving communication security and reducing power consumption of the system, fueling new research in these areas. In this paper, we first provide a comprehensive survey and classification of commercially available wearables and research prototypes. We then examine the communication security issues facing the popular wearables followed by a survey of solutions studied in the literature. We also categorize and explain the techniques for improving the power efficiency of wearables. Next, we survey the research literature in wearable computing. We conclude with future directions in wearable market and research.
TL;DR: The results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps.
Abstract: We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).