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Subject matter expert in Mobile and Ubiquitous Computing
Human Mobility Characterization from Cellular Network Data
Richard Becker, Ramon Caceres, Karrie Hanson, Sibren Isaacman, Ji Loh, Margaret Martonosi, James Rowland, Simon Urbanek, Alexander Varshavsky, Christopher Volinsky
Communications of the ACM,
2013.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Communications of the ACM , Volume 56, Issue 1, 2013-01-01.
{Characterizing human mobility patterns is critical to a deeper understanding of the effects of people’s travel on society and the environment. Location data from cellular telephone networks can shed light on human movements cheaply, frequently, and on a large scale. We have developed techniques for analyzing anonymized cellphone locations to explore various aspects of human mobility, in particular for hundreds of thousands of people in each of the Los Angeles, San Francisco, and New York metropolitan areas. Our results include measures of how far people travel every day, estimates of carbon footprints due to home-to-work commutes, maps of the residential areas that contribute workers to a city, and relative traffic volumes on commuting routes. We have validated the accuracy of our techniques through comparisons against ground truth provided by volunteers and against independent sources such as the US Census Bureau. Throughout our work, we have taken measures to preserve the privacy of cellphone users. This article presents an overview of our methodologies and findings.}
TapPrints: Your Finger Taps have Fingerprints
Emiliano Miluzzo, Alexander Varshavsky, Suhrid Balakrishnan, Romit Roy Choudhury
The 10th International Conference on Mobile Systems, Applications and Services (MobiSys 2012),
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in The 10th International Conference on Mobile Systems, Applications and Services (MobiSys 2012) , 2012-06-26.
{This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the user�s inputs, such as keyboard presses and icon taps. While precise tap detection is non-trivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch- screens using motion sensor data combined with machine learning analysis. By running tests on two different off-the-shelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90% and 80% accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.}
Obtaining In-Context Measurements of Cellular Network Performance
Aaron Gember, Jeffrey Pang, Alexander Varshavsky, Ramon Caceres, Aditya Akella
ACM Internet Measurement Conference,
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2012 , 2012-11-14.
{Network service providers, and other parties, require an accurate understanding of the performance cellular networks deliver to users. In particular, they often seek a measure of the network performance users experience solely when they are interacting with their device—a measure we call in-context. Acquiring such measures is challenging due to the many factors, including time and physical context, that influence cellular network performance. This paper makes two contributions. First, we conduct a large scale measurement study, based on data collected from a large cellular provider and from hundreds of controlled experiments, to shed light on the issues underlying in-context measurements. Our novel observations show that measurements must be conducted on devices which (i) recently used the network as a result of user interaction with the device, (ii) remain in the same macro-environment (e.g., indoors and stationary), and in some cases the same micro-environment (e.g., in the user’s hand), during the period between normal usage and a subsequent measurement, and (iii) are currently sending/receiving little or no user-generated traffic. Second, we design and deploy a prototype active measurement service for Android phones based on these key insights. Our analysis of 1650 measurements gathered from 12 volunteer devices shows that the system is able to obtain average throughput measurements that accurately quantify the performance experienced during times of active device and network usage.}
Managing Cellular Congestion Using Incentives
Yih Chen, Rittwik Jana, Daniel Stern, Alexander Varshavsky, Bin Wei, Jagadeesh Dyaberi, Karthik Kannan, Vijay Pai
IEEE Communications Magazine - Special Issue on Communications Network Economics,
2012.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012 , Volume 50, Issue 11, 2012-10-01, http://www.comsoc.org/commag/
{Mobile data traffic is expected to grow exponentially in the next few years due to the explosive growth of Mobile Web and video traffic on smartphones.
Wireless operators have invested heavily to make infrastructural improvements by installing new cell towers and offloading cellular data traffic to Wi-Fi to resolve congestion. They are also exploring the use of behavioral and economic interventions to manage congestions. To understand the role of interventions, we distributed smartphones to students at Purdue University,
loaded with applications to perform monitoring and location tracking with user consent. We conducted two experiments: first with 14 phones of one type, then with 30 phones of two types. Wi-Fi traffic and cellular network data usage were collected and analyzed to characterize and quantify the changes in usage behaviors; the second experiment also captured location data during compliance/non-compliance to incentive messages. The trial seeks not only to experiment with incentives and disincentives to observe their
effectiveness, but also to understand current mobile broadband and Wi-Fi usage behaviors in a campus environment. Our results indicate a high level of compliance with economic incentives and disincentives.
Detailed analysis further showed correlation with two psychological measures of each user (agreeableness and neuroticism). In addition, we found schemes with probabilistic payments of higher incentive amounts getting more positive results compared to schemes with definite payments with lower incentive amounts - despite similar total payout.}
Human Mobility Modeling at Metropolitan Scales
Sibren Isaacman, Richard Becker, Ramon Caceres, Margaret Martonosi, James Rowland, Alexander Varshavsky, Walter Willinger
10th ACM International Conference on Mobile Systems, Applications and Services (MobiSys 2012),
2012.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in [10th ACM International Conference on Mobile Systems, Applications and Services (MobiSys 2012)] , 2012-06-26.
{Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surround- ing real CDRs.}
Traffic Backfilling: Subsidizing Lunch for Delay-Tolerant Applications in UMTS Networks
Horacio Lagar , Kaustubh Joshi, Alexander Varshavsky, Jeffrey Bickford, Darwin Parra
ACM MobiHeld workshop 2011,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM MobiHeld workshop 2011 , 2011-10-23.
{Mobile application developers pay little attention to the interactions between applications and the cellular net- work carrying their traffic. This results in wastage of de- vice energy and network signaling resources. We place part of the blame on mobile OSes: they do not expose adequate interfaces through which applications can in- teract with the network. We propose traffic backfilling, a technique in which delay-tolerant traffic is opportunis- tically transmitted by the OS using resources left over by the naturally occurring bursts caused by interactive traffic. Backfilling presents a simple interface with two classes of traffic, and grants the OS and network large flexibility to maximize the use of network resources and reduce device energy consumption. Using device traces and network data from a major US carrier, we demon- strate a large opportunity for traffic backfilling.}
Security versus Energy Tradeoffs in Host-Based Mobile Malware Detection
Horacio Lagar , Alexander Varshavsky, Jeffrey Bickford, Vinod Ganapathy, Liviu Iftode
ACM Conference on Mobile Systems, Applications and Services (MobiSys),
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Conference on Mobile Systems, Applications and Services (MobiSys) , 2011-06-29.
{The rapid growth of mobile malware necessitates the presence of robust malware detectors on mobile devices. However, running malware detectors on mobile devices may drain their battery, causing users to disable these protection mechanisms to save power. This paper studies the security versus energy tradeoffs for a particularly challenging class of malware detectors, namely rootkit detectors. Specifically, we investigate the security/energy tradeoffs along two axes: attack surface and malware scanning frequency, for both code and data based rootkit detectors. Our findings, based on a real implementation on a phone-like device, reveal that protecting against code-driven attacks is relatively cheap, while protecting against all data-driven attacks is prohibitively expensive. Based on our findings, we determine a sweet spot in the security/energy tradeoff, called the balanced profile, which protects a mobile device against a vast majority of attacks, while consuming limited amount of extra battery power. }
Route Classification using Cellular Handoff Patterns
Christopher Volinsky, Alexander Varshavsky, Richard Becker, Ji Loh, Simon Urbanek, Ramon Caceres, Karrie Hanson
13th ACM International Conference on Ubiquitous Computing,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 13th ACM International Conference on Ubiquitous Computing , 2011-09-01.
{Understanding utilization of city roads is important for urban planners. In this paper, we show how to use cellular hand- off patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes the following three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Mover�s Distance. Second, we present two accurate classification al- gorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for mea- suring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statis- tics published by a state transportation authority.}
ProxiMate: Proximity-based Secure Pairing using Ambient Wireless Signals
Suhas Mathur, Rob Miller, Alexander Varshavsky, Wade Trappe, Narayan Mandayam
MobiSys 2011,
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MobiSys2011 , 2011-06-28.
Forming secure associations between wireless devices that do not share a prior trust relationship is an important problem. This paper presents ProxiMate, an algorithm that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment. The shared key synthesized by ProxiMate can be used by the devices to authenticate each others' physical proximity and then to communicate confidentially. Unlike traditional pairing approaches such as Diffie-Hellman, ProxiMate is secure against a computationally unbounded adversary and its computational complexity is linear in the size of the key. We evaluate ProxiMate using an experimental prototype built using an open-source software-defined platform and demonstrate its effectiveness in generating common secret bits. We further show that it is possible to speed up secret key synthesis by monitoring multiple RF sources simultaneously or by shaking together the devices that need to be paired. Finally, we show that ProxiMate is resistant to even the most powerful attacker who controls the public RF source used by legitimate devices for pairing.
Predicting Handoffs in 3G Networks
Ramon Caceres, Jeffrey Pang, Alexander Varshavsky, Umar Javed, Dongsu Han, Srinivasan Sesah
3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld),
2011.
[PDF]
[BIB]
ACM Copyright
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld) , 2011-10-23.
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Clustering Anonymized Mobile Call Detail Records to Find Usage Groups
Christopher Volinsky, Richard Becker, Ramon Caceres, Karrie Hanson, Ji Loh, Simon Urbanek, Alexander Varshavsky
1st Workshop on Pervasive Urban Applications (PURBA),
2011.
[PDF]
[BIB]
Springer Copyright
The definitive version was published in PURBA-2011. , 2011-06-12
{Understanding the mix of different types of people in a city is an important input into urban planning. In this paper we identify distinct sectors of a population by their cellular phone usage. In a study of a small suburban city in New Jersey, we use unsupervised clustering to identify the usage patterns of heavy users . We uncover 7 unique usage patterns. We interpret two of the patterns as belonging to commuters and students, and verify these interpretations with deeper analysis of temporal and spatial patterns. }
A Tale of One City: Using Cellular Network Data for Urban Planning
Richard Becker, Ramon Caceres, Karrie Hanson, Ji Loh, Simon Urbanek, Alexander Varshavsky, Christopher Volinsky
IEEE Pervasive Computing ,
2010.
[PDF]
[BIB]
IEEE Copyright
The definitive version was published in IEEE Pervasive Computing , 2010-04-01, URL: https://ecopyright.ieee.org/ECTT/login.jsp Username: SCHPCSI-2011-01-0005 Password: 1295115660850
{The rapid growth of modern cities leaves urban planners faced with numerous challenges, such as high congestion and pollution levels. Effectively solving these challenges re- quires a deep understanding of existing city dynamics. In this paper, we describe methodology to study and monitor these dynamics by using Call Detail Records (CDRs), rou- tinely collected by wireless service providers as part of run- ning their networks. Our methodology scales to an entire population, has little additional cost, and can be continually updated. This provides an unprecedented opportunity to study and monitor cities in a way that current practices are not able to do.}
