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.

WiFi is a major source of energy consumption on smartphones. Unfortunately, a non-negligible portion of the WiFi energy consumption is spent for frames that are useless to the smartphone. For example, energy is wasted to receive WiFi broadcast frames that are not needed by any smartphone application. What's worse, in order to process the broadcast frames received, a smartphone in suspend mode switches from suspend mode to high power active mode and stays there for a while. As such, additional energy is wasted to do the processing. In this paper, we design a system, namely HIDE, to reduce smartphone energy wasted on useless WiFi broadcast traffic. With our system, smartphones in suspend mode do not receive useless broadcast frames or wake up to process useless broadcast frames. Our trace-driven simulation shows that the HIDE system saves 34%-75% energy for Nexus One when 10% of the broadcast frames are useful to the smartphone. Our overhead analysis demonstrates that our system has negligible impact on network capacity and packet round-trip time.

Mobile video chat apps offer users an approachable way to communicate with others. As high-speed 4G networks are being deployed worldwide, the number of mobile video chat app users increases. However, video chatting on mobile devices brings users financial concerns, since streaming video demands high bandwidth and can use up a large amount of data in dozens of minutes. Lowering the bandwidth usage of mobile video chats is challenging since video quality may be compromised. In this paper, we attempt to tame this challenge. Technically, we propose a context-aware frame rate adaption framework, named low-bandwidth video chat (LBVC). It follows a sender-receiver cooperative principle that smartly handles the tradeoff between lowering bandwidth usage and maintaining video quality. We implement LBVC by modifying an open-source app–Linphone– and evaluate it with both objective experiments and subjective studies.

Existing research has demonstrated that inter-body sensor network (inter-BSN) information sharing among coexisting BSNs can enhance applications' performance and save energy. However, how to achieve effective inter-BSN information sharing through wireless communication is a challenging task. On one hand, a BSN should be able to discover neighboring BSNs and establish inter-BSN links with quality of service (QoS) assurances. On the other hand, a BSN should be able to prevent the QoS of intraand inter-BSN links from being degraded by multiple BSNs' mutual interference. In this paper, we propose BuddyQoS, a framework that provides network throughput assurances for coexisting and shared buddy BSNs. In particular, BuddyQoS accurately estimates and adaptively schedules wireless resources to meet the throughput requirements of all interand intra-BSN links. Our trace-driven experiment results demonstrate that BuddyQoS outperforms the default CSMA solution in the standard TinyOS-2.x releases in terms of providing throughput assurances.

Frequent route is an important individual outdoor behavior pattern that many trajectory-based applications rely on. In this paper, we propose a novel framework for extracting frequent routes from personal GPS trajectories. The key idea of our design is to accurately detect road corners and utilize these new metaphors to tackle the problem of frequent route extraction. Concretely, our framework contains three phases: 1) characteristic point (CP) extraction; 2) corner detection; and 3) trajectory mapping. In the first phase, we present a linear fitting-based algorithm to extract CPs. In the second phase, we develop a multiple density level DBSCAN (density-based spatial clustering of applications with noise) algorithm to locate road corners by clustering CPs. In the third phase, we convert each trajectory into an ordered sequence of road corners and obtain all routes that have been traversed by an individual for at least ${F}$ (frequency threshold) times. We evaluate the framework using real-world trajectory datasets of individuals for one year and the experimental results demonstrate that our framework outperforms the baseline approach by 7.8% on average in terms of precision and 21.9% in terms of recall.

Smartphones save energy by entering a low power suspend mode (120mW). As a result, power consumption increases dramatically. To improve energy efficiency, some phones employ a hardware broadcast filter in the WiFi driver. All UDP broadcast frames other than Multicast DNS frames are blocked, thus none is received by upper layer applications. So, we have a dilemma of handling WiFi broadcast traffic during smartphone suspend mode: either receive all of them suffering high power consumption, or receive none of them sacrificing functionalities. In this paper, we propose Software Broadcast Filter (SBF) to address the dilemma. SBF is smarter than the hardware broadcast filter as it only filters out useless broadcast frames and does not impair functionalities of applications. SBF is also more energy efficient than the “receive all” method. Our trace driven evaluation shows that SBF can save up to 52% energy consumption than the “receive all” method.

Video chat apps enable users to stay in touch with their family, friends and colleagues. However, they consume a lot of bandwidth and hence can quickly use up a monthly data plan quota, which is a high-cost resource on smartphones. In this paper, we propose LBVC (Low-bandwidth Video Chat), a user-guided vibration-aware frame rate adaption framework. LBVC takes a sender-receiver cooperative approach and reduces bandwidth usage as well as alleviates video quality degradation for video chat apps on smartphones. We implement LBVC on the Android platform and evaluate its performance on a series of experiments and user study with 21 pairs of subjects. Compared to the default solution, LBVC decreases bandwidth usage by 35% and at the same time maintains good video quality without introducing extra power consumption under typical video chat scenarios.

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%.

WiFi effectively has two extremes: low power consumption and high latency, or low latency and high power consumption. WiFi Power Save Mode saves energy by trading added latency for less power consumption. Minimal latency but maximum power, on the other hand, is consumed with WiFi Active Mode. While research has advanced in mitigating these extremes, certain types of network traffic such as constant bitrate streaming make the contrast unavoidable. We introduce Bluesaver, which provides low latency and low energy by maintaining a Bluetooth and WiFi connection simultaneously. Bluesaver is designed at the MAC layer and is able to opportunistically select the most efficient connection for packets while still assuring acceptable latency. We implement Bluesaver on an Android phone and Access Point and show that we can save more than 25% energy over existing solutions and attain the capability of quickly adapting to changes in network traffic.

Continuous authentication modalities allow a device to authenticate users transparently without interrupting them or requiring their attention. This is especially important on smartphones, which are more prone to be lost or stolen than regular computers, and carry plenty of sensitive information. There is a multitude of signals that can be harnessed for continuous authentication on mobile devices, such as touch input, accelerometer, and gyroscope, etc. However, existing public datasets include only a handful of them, limiting the ability to do experiments that involve multiple modalities. To fill this gap, we performed a large-scale user study to collect a wide spectrum of signals on smartphones. Our dataset combines more modalities than existing datasets, including movement, orientation, touch, gestures, and pausality. This dataset has been used to evaluate our new behavioral modality named Hand Movement, Orientation, and Grasp (H-MOG). This poster reports on the data collection process and outcomes, as well as preliminary authentication results.

With newly developed wireless neuroheadsets, electroencephalography (EEG) neurometrics can be incorporated into in situ and ubiquitous physiological monitoring for human mental health. As a resource constraint system providing critical health services, the EEG headset design must consider both high application fidelity and energy efficiency. However, through empirical studies with an off-the-shelf Emotiv EPOC Neuroheadset, we uncover a mismatch between lossy EEG sensor communication and high neurometric application fidelity requirements. To tackle this problem, we study how to learn the sensitivity of neurometric application fidelity to EEG data. The learned sensitivity is used to develop two algorithms: 1) an energy minimization algorithm minimizing the energy usage in EEG sampling and networking while meeting applications' fidelity requirements and 2) a fidelity maximization algorithm maximizing the sum of all applications' fidelities through the incorporation and optimal utilization of a limited data buffer. The effectiveness of our proposed solutions is validated through trace-driven experiments.

Wireless sensor network applications, such as those for natural disaster warning, vehicular traffic monitoring, and surveillance, have stringent accuracy requirements for detecting or classifying events and demand long system lifetimes. Through quantitative study, we show that existing event detection approaches are challenged to explore the sensing capability of a deployed system and choose the right sensors to meet user-specified accuracy. Event detection systems are also challenged to provide a generic system that efficiently adapts to environmental dynamics and works easily with a range of applications, machine learning approaches, and sensor modalities. Consequently, we propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user-specified detection accuracy during runtime while significantly reducing energy consumption. Watchdog can use different machine learning techniques to learn the sensing capability of a heterogeneous sensor deployment and meet accuracy requirements. To address environmental dynamics and ensure energy savings, Watchdog wakes up and puts to sleep sensors as needed to meet user-specified accuracy. Through evaluation with real vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user-specified detection accuracy, energy savings, and environmental adaptability.

In this paper, to our best knowledge, we are first to provide an experimental study on how storage techniques affect power levels in smartphones and introduce energy-efficient approaches to reduce energy consumption. We evaluate power degradation at several layers of block I/O, focusing on the block layer and device driver. At each level, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. The system tracks the run-time I/O pattern of a smartphone that is then matched with the closest pattern from the benchmark table. After having obtained the optimal parameters, it dynamically configures storage parameters to reduce energy consumption. We evaluate our prototype by using the 20 most popular Android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques.

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.

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.

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.

Body sensor networks (BSNs) and WiFi networks have been widely investigated due to the availability of sensor motes and WiFi devices, but they are commonly deployed separately. In this paper we propose to optimize the total communication energy consumption of BSN and WiFi (BSN-WiFi) networks using joint data rate adaptation. More specifically, we first elaborate the BSN-WiFi network system in four consecutive phases. Then based on the system, we analyze the communication energy consumption, throughput and time delay, and provide a signal-to-noise ratio and packet delivery ratio (SNR-PDR) mappings of BSN and WiFi networks. Next, we build an energy optimization model with constraints of SNR-PDR mappings, throughput, and time delay to minimize the total communication energy consumption in BSN-WiFi networks. With the input of SNR values, we solve this model by cvx to obtain the output of optimal data rates associated with SNR values, which are then tabulated for online data rate adaptation. Finally, we collect 20-minute traces from a specific BSN-WiFi network system for performance evaluation, and the results demonstrate that our optimal data rate solution achieves up to 86% energy savings comparing with the solutions using fixed data rates.

Automatically recognizing human activities in a body sensor network (BSN) enables many human-centric applications. Many current works recognize human activities through collecting and analyzing sensor readings from on-body sensor nodes. These sensing-based solutions face a dilemma. On one hand, to guarantee data availability and recognition accuracy, sensing-based solutions have to either utilize a high transmission power or involve a packet retransmission mechanism. On the other hand, enhancing the transmission power increases a sensor node's energy overheads and communication range. The enlarged communication range in consequence increases privacy risks. A packet retransmission mechanism complicates on-body sensor nodes' MAC layer and hence increases energy overheads. In contrast to the sensing-based solutions, we build Radio Sense, a prototype system that exploits wireless communication patterns for BSN activity recognition. Using Radio Sense, we benchmark three system parameters (transmission (TX) power, packet sending rate, and smoothing window size) to design algorithms for system parameter selection. The algorithms aim to balance accuracy, latency, and energy overheads. In addition, we investigate the minimal amount of training data needed for reliable performance. We evaluate our Radio Sense system with multiple subjects' data collected over a two-week period and demonstrate that Radio Sense achieves reliable performance in terms of accuracy, latency, and battery lifetime.

In this paper, we propose to optimize energy consumption in heterogeneous wireless networks through joint packet size optimization. Specifically, we consider a two-hop data communication system composed of a body sensor network (BSN) and a WiFi network. Within the system, we formulate an energy consumption optimization problem with the constraints of both throughput and time delay. Mathematically, we convert this problem into a geometric programming (GP) problem, which is then numerically solved. The solutions can be used by both the BSN and the WiFi network to dynamically change their packets' payload sizes based on their current packet delivery ratios (PDRs). Since the PDRs are time-varying, we tabulate an offline payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed two-hop BSN-WiFi network and simulate the energy consumption. The performance evaluation results show that our solution achieves up to 70% energy savings compared with solutions that use fixed packet sizes.