As scientific data volumes exponentially grow, dynamic, flexible and open approaches to data governance are needed. In this paper, we describe our efforts to build an open, scientific Modern Data Governance Framework (mDGF) that streamlines and makes actionable data governance requirements for projects and data providers. We present the goals and design of the mDGF. We also share our envisioned usage for the mDGF and planned future work.

Garbled circuits are general tools that allow two parties to compute any function without disclosing their respective inputs. Applications of this technique vary from distributed privacy-preserving machine learning tasks to secure outsourced authentication. Unfortunately, the energy cost of garbled circuit evaluation protocols is substantial. This limits the applicability of garbled circuits in scenarios that involve battery-operated devices, such as Internet-of-Things (IoT) devices and smartphones. In this paper, we propose MEG, a Memory- and Energy-efficient Garbled circuit evaluation mechanism. MEG utilizes batch data transmission and multi-threading to reduce memory and energy consumption. We implement MEG on an Android smartphone and compare its performance and energy consumption with state-of-the-art techniques using two garbled circuits of widely different sizes (AES-128 and 256-bit edit distance). Our results show that, compared with “plain” garbled circuit evaluation, MEG decreases memory consumption by more than 90%. When compared with current pipelined garbled circuit evaluation techniques, MEG's energy usage was 42% lower for AES-128 and 23% lower for EDT-256. Furthermore, our multi-thread implementation of MEG decreased circuit evaluation time by up to 56.7% for AES-128, and by up to 13.5% for EDT-256, compared with state-of-the-art pipelining techniques.

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.

Our study reveals that facilitating warm launch of just five smartphone applications is extremely expensive, using up to 36 percent of memory. Further investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Therefore, we present iRAM, a personalized system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation on memory hungry applications indicates that iRAM reduces application crashes by up to 14 percent, and reduces launch delays by up to 78.2 percent. In addition, the results confirm that iRAM increases free memory levels by up to 4.8 times. This performance gain comes with 3.5 percent of CPU overhead and 0.9 percent of power overhead.

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).

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.

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%.

No abstract available.

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.

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.

A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentrations are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemispheres from 09 July 1987 to 31 December 2007 with an update through 2012 underway. The NetCDF data files along with detailed data processing steps and error source information can be found at http://dx.doi.org/10.7265/N5B56GN3. The dataset is archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic statistical characteristics of the CDR are presented here. Our analysis results have shown that while the Northern Hemisphere experiences diminishing sea ice extent for the two decades from January 1988 to December 2007 with a faster reduction rate for the annual minimum sea ice extent, the Southern Hemisphere has experienced slightly increase in its sea ice extent for the same period, especially for the annual maximum.

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.

Daily 2009 surface winds from five model, remotely-sensed, and reanalysis wind products are compared with measurements from three fixed mooring buoys located in the west equatorial Pacific (0°N, 165°E), the central equatorial Pacific (0°N, 170°W), and the eastern-central equatorial Pacific Ocean (0°N, 140°W). The cross-correlation coefficients between these five wind products and the buoy winds are all statistically significant at the 95% confidence level, ranging from 0.77 to 0.97 for the zonal winds and 0.45 to 0.93 for the meridional winds. However, a systematic positive/negative bias was found in wind directions in all wind products at the central/eastern central Pacific Ocean, which could have an impact on local wind stress curl field in the region.

To address the selfishness issue in application-layer multicast, we present a selfishness-aware application-layer multicast (SAM). SAM defines an altruism value for each node associative to its contributions to the system. Nodes are first partitioned into topologically-aware clusters using the binning scheme. Within the cluster, the subtree is constructed to place the nodes with greater altruism value at the higher layer of the tree. As compared to other studies in this area, SAM exhibits innovative advantages in both altruism value computation and multicast tree construction. Firstly, the node's altruism value is generated from the feedback from its parent and children which enables the system to detect the selfish nodes effectively. Peers don't need the extra probe messages to measure the QoS of their neighbors. During the process of tree construction and maintenance, only O(logN) nodes need to be adjusted. Lastly, the altruism value calculation and multicast tree construction are realized in a decentralized manner without any single point of failure. Simulation results show that even with a significant portion of nodes being selfish, SAM is able to build a dissemination tree that provides high overall streaming quality with low control overhead.