arXiv subjects

Yuvraj Agarwal

Publications and source records attributed to Yuvraj Agarwal.

30 records · Page 2Linked to original sources

arXiv2019

Real World Longitudinal iOS App Usage Study at Scale

Given the importance of understanding the interaction between mobile devices and their users, app usage patterns have been studied in various contexts. However, prior work has not fully investigated longitudinal changes to app usage behavior. In this paper, we present a longitudinal, large-scale study of mobile app usage based on a dataset collected from 162,006 iPhones and iPads over 4 years. We explore multiple dimensions of app usage pattern proving useful insights on how app usage changes over time. Our key findings include (i) app usage pattern changes over time both at the individual app level and the app category level (i.e. proportion of time a user spends using an app), (ii) users keep a small set of apps frequently launched (90% of iPhone users launch roughly 14-18 apps weekly), (iii) a small number of apps remain popular while some specific kinds of apps (e.g. Games) have a shorter life cycle compared to other apps of different categories. Finally, we discuss our findings and their implications, for example, a short-term study as an attempt to understand the general needs of mobile devices may not achieve useful results for the long term.

cs.HC↗

arXiv2019

Demo Abstract: Pible: Battery-Free Mote for Perpetual Indoor BLE Applications

As of today, large-scale wireless sensor networks are adopted for smart building applications as they are easy and flexible to deploy. Low-power wireless nodes can achieve multi-year lifetimes with an AA battery using Bluetooth Low Energy (BLE) and Zig-Bee. However, replacing these batteries at scale is a non-trivial, labor-intensive task. Energy harvesting has emerged as a potential solution to avoid battery replacement but requires compromises such as application specific sensor node design, simplified communication protocol or reduced quality of service. We show the design of a battery-free sensor node using commercial off the shelf components, and present Pible: a Perpetual Indoor BLE sensor node that uses an ambient light energy harvesting system and can support numerous smart building applications. We show trade-offs between node-lifetime, quality of service and light availability and present a predictive algorithm that adapts to changing lighting conditions to maximize node lifetime and application quality of service.

cs.NI↗

arXiv2018

Pible: Battery-Free Mote for Perpetual Indoor BLE Applications

Smart building applications require a large-scale deployment of sensors distributed across the environment. Recent innovations in smart environments are driven by wireless networked sensors as they are easy to deploy. However, replacing these batteries at scale is a non-trivial, labor-intensive task. Energy harvesting has emerged as a potential solution to avoid battery replacement but requires compromises such as application specific design, simplified communication protocol or reduced quality of service. We explore the design space of battery-free sensor nodes using commercial off the shelf components, and present Pible: a Perpetual Indoor BLE sensor node that leverages ambient light and can support numerous smart building applications. We analyze node-lifetime, quality of service and light availability trade-offs and present a predictive algorithm that adapts to changing lighting conditions to maximize node lifetime and application quality of service. Using a 20 node, 15-day deployment in a real building under varying lighting conditions, we show feasible applications that can be implemented using Pible and the boundary conditions under which they can fail.

cs.NI↗

arXiv2018

PrivacyProxy: Leveraging Crowdsourcing and In Situ Traffic Analysis to Detect and Mitigate Information Leakage

Many smartphone apps transmit personally identifiable information (PII), often without the users knowledge. To address this issue, we present PrivacyProxy, a system that monitors outbound network traffic and generates app-specific signatures to represent sensitive data being shared. PrivacyProxy uses a crowd-based approach to detect likely PII in an adaptive and scalable manner by anonymously combining signatures from different users of the same app. Furthermore, we do not observe users network traffic and instead rely on hashed signatures. We present the design and implementation of PrivacyProxy and evaluate it with a lab study, a field deployment, a user survey, and a comparison against prior work. Our field study shows PrivacyProxy can automatically detect PII with an F1 score of 0.885. PrivacyProxy also achieves an F1 score of 0.759 in our controlled experiment for the 500 most popular apps. The F1 score also improves to 0.866 with additional training data for 40 apps that initially had the most false positives. We also show performance overhead of using PrivacyProxy is between 8.6% to 14.2%, slightly more than using a standard unmodified VPN, and most users report no perceptible impact on battery life or the network.

cs.CR↗

arXiv2018

Automated Extraction of Personal Knowledge from Smartphone Push Notifications

Personalized services are in need of a rich and powerful personal knowledge base, i.e. a knowledge base containing information about the user. This paper proposes an approach to extracting personal knowledge from smartphone push notifications, which are used by mobile systems and apps to inform users of a rich range of information. Our solution is based on the insight that most notifications are formatted using templates, while knowledge entities can be usually found within the parameters to the templates. As defining all the notification templates and their semantic rules are impractical due to the huge number of notification templates used by potentially millions of apps, we propose an automated approach for personal knowledge extraction from push notifications. We first discover notification templates through pattern mining, then use machine learning to understand the template semantics. Based on the templates and their semantics, we are able to translate notification text into knowledge facts automatically. Users' privacy is preserved as we only need to upload the templates to the server for model training, which do not contain any personal information. According to our experiments with about 120 million push notifications from 100,000 smartphone users, our system is able to extract personal knowledge accurately and efficiently.

cs.IR↗

arXiv2018

Oh, What a Fragile Web We Weave: Third-party Service Dependencies In Modern Webservices and Implications

The recent October 2016 DDoS attack on Dyn served as a wakeup call to the security community as many popular and independent webservices (e.g., Twitter, Spotify) were impacted. This incident raises a larger question on the fragility of modern webservices due to their dependence on third-party services. In this paper, we characterize the dependencies of popular webservices on third party services and how these can lead to DoS, RoQ attacks, and reduction in security posture. In particular, we focus on three critical infrastructure services: DNS, CDNs, and certificate authorities (CAs). We analyze both direct relationships (e.g., Twitter uses Dyn) and indirect dependencies (e.g., Netflix uses Symantec as OCSP and Symantec, in turn, uses Verisign for DNS). Our key findings are: (1) 73.14% of the top 100,000 popular services are vulnerable to reduction in availabil- ity due to potential attacks on third-party DNS, CDN, CA services that they exclusively rely on; (2) the use of third-party services is concentrated, so that if the top-10 providers of CDN, DNS and OCSP services go down, they can potentially impact 25%-46% of the top 100K most popular web services; (3) transitive depen- dencies significantly increase the set of webservices that exclusively depend on popular CDN and DNS service providers, in some cases by ten times (4) targeting even less popular webservices can potentially cause signifi- cant collateral damage, affecting upto 20% of the top- 100K webservices due to their shared dependencies. Based on our findings, we present a number of key implications and guidelines to guard against such Internet- scale incidents in the future.

cs.NI↗

arXiv2017

Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis

Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configuration space. Recently, transfer learning has been applied to reduce the effort of constructing performance models by transferring knowledge about performance behavior across environments. While this line of research is promising to learn more accurate models at a lower cost, it is unclear why and when transfer learning works for performance modeling. To shed light on when it is beneficial to apply transfer learning, we conducted an empirical study on four popular software systems, varying software configurations and environmental conditions, such as hardware, workload, and software versions, to identify the key knowledge pieces that can be exploited for transfer learning. Our results show that in small environmental changes (e.g., homogeneous workload change), by applying a linear transformation to the performance model, we can understand the performance behavior of the target environment, while for severe environmental changes (e.g., drastic workload change) we can transfer only knowledge that makes sampling more efficient, e.g., by reducing the dimensionality of the configuration space.

stat.ML↗

arXiv2017

A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings

Heating, Ventilation, and Cooling (HVAC) systems are often the most significant contributor to the energy usage, and the operational cost, of large office buildings. Therefore, to understand the various factors affecting the energy usage, and to optimize the operational efficiency of building HVAC systems, energy analysts and architects often create simulations (e.g., EnergyPlus or DOE-2), of buildings prior to construction or renovation to determine energy savings and quantify the Return-on-Investment (ROI). While useful, these simulations usually use static HVAC control strategies such as lowering room temperature at night, or reactive control based on simulated room occupancy. Recently, advances have been made in HVAC control algorithms that predict room occupancy. However, these algorithms depend on costly sensor installations and the tradeoffs between predictive accuracy, energy savings, comfort and expenses are not well understood. Current simulation frameworks do not support easy analysis of these tradeoffs. Our contribution is a simulation framework that can be used to explore this design space by generating objective estimates of the energy savings and occupant comfort for different levels of HVAC prediction and control performance. We validate our framework on a real-world occupancy dataset spanning 6 months for 235 rooms in a large university office building. Using the gold standard of energy use modeling and simulation (Revit and Energy Plus), we compare the energy consumption and occupant comfort in 29 independent simulations that explore our parameter space. Our results highlight a number of potentially useful tradeoffs with respect to energy savings, comfort, and algorithmic performance among predictive, reactive, and static schedules, for a stakeholder of our building.

eess.SY↗

arXiv2016

Managing Commercial HVAC Systems: What do Building Operators Really Need?

Buildings form an essential part of modern life; people spend a significant amount of their time in them, and they consume large amounts of energy. A variety of systems provide services such as lighting, air conditioning and security which are managed using Building Management Systems (BMS) by building operators. To better understand the capability of current BMS and characterize common practices of building operators, we investigated their use across five institutions in the US. We interviewed ten operators and discovered that BMS do not address a number of key concerns for the management of buildings. Our analysis is rooted in the everyday work of building operators and highlights a number of design suggestions to help improve the user experience and management of BMS, ultimately leading to improvements in productivity, as well as buildings comfort and energy efficiency.

cs.CY↗

arXiv2016

Quiver: Using Control Perturbations to Increase the Observability of Sensor Data in Smart Buildings

Modern buildings consist of hundreds of sensors and actuators for monitoring and operation of systems such as HVAC, light and security. To enable portable applications in next generation smart buildings, we need models and standardized ontologies that represent these sensors across diverse types of buildings. Recent research has shown that extracting information such as sensor type with available metadata and timeseries data analysis is difficult due to heterogeneity of systems and lack of support for interoperability. We propose perturbations in the control system as a mechanism to increase the observability of building systems to extract contextual information and develop standardized models. We design Quiver, an experimental framework for actuation of building HVAC system that enables us to perturb the control system safely. Using Quiver, we demonstrate three applications using empirical experiments on a real commercial building: colocation of data points, identification of point type and mapping of dependency between actuators. Our results show that we can colocate data points in HVAC terminal units with 98.4 % accuracy and 63 % coverage. We can identify point types of the terminal units with 85.3 % accuracy. Finally, we map the dependency links between actuators with an accuracy of 73.5 %, with 8.1 % and 18.4 % false positives and false negatives respectively.

eess.SY↗

arXiv2016

Genie: A Longitudinal Study Comparing Physical and Software-augmented Thermostats in Office Buildings

Thermostats are primary interfaces for occupants of office buildings to express their comfort preferences. However, standard thermostats are often ineffective due to inaccessibility, lack of information, or limited responsiveness, leading to occupant discomfort. Software thermostats based on web or smartphone applications provide alternative interfaces to occupants with minimal deployment cost. However, their usage and effectiveness have not been studied extensively in real settings. In this paper we present Genie, a novel software-augmented thermostat that we deployed and studied at our university over a period of 21 months. Our data shows that providing wider thermal control to users does not lead to system abuse and that the effect on energy consumption is minimal while improving comfort and energy awareness. We believe that increased introduction of software thermostats in office buildings will have important effects on comfort and energy consumption and we provide key design recommendations for their implementation and deployment.

cs.HC↗

arXiv2015

HVACMeter: Apportionment of HVAC Power to Thermal Zones and Air Handler Units

Heating, Ventilation and Air Conditioning (HVAC) systems consume almost half of the total energy use of commercial buildings. To optimize HVAC energy usage, it is important to understand the energy consumption of individual HVAC components at fine granularities. However, buildings typically only have aggregate building level power and thermal meters. We present HVACMeter, a system which leverages existing sensors in commercial HVAC systems to estimate the energy consumed by individual components of the HVAC system, as well by each thermal zone in buildings. HVACMeter can be generalized to any HVAC system as it uses the basic understanding of HVAC operation, heat transfer equations, and historical sensor data to estimate energy. We deploy HVACMeter to three buildings on our campus, to identify the set of sensors that are important for accurately disaggregating energy use at the level of each Air Handler Unit and each thermal zone within these buildings. HVACMeter power estimations have on an average 44.5 % less RMSE than that of mean power estimates. Furthermore, we highlight the usefulness of HVACMeter energy estimation model for a building fault detection application by quantifying the amount of energy that can be saved by fixing particular faults.

eess.SY↗