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Mobile Systems

Mobile devices are the prevalent computing device in many parts of the world, and over the coming years it is expected that mobile Internet usage will outpace desktop usage worldwide. Google is committed to realizing the potential of the mobile web to transform how people interact with computing technology. Google engineers and researchers work on a wide range of problems in mobile computing and networking, including new operating systems and programming platforms (such as Android and ChromeOS); new interaction paradigms between people and devices; advanced wireless communications; and optimizing the web for mobile settings. In addition, many of Google’s core product teams, such as Search, Gmail, and Maps, have groups focused on optimizing the mobile experience, making it faster and more seamless. We take a cross-layer approach to research in mobile systems and networking, cutting across applications, networks, operating systems, and hardware. The tremendous scale of Google’s products and the Android and Chrome platforms make this a very exciting place to work on these problems.

Some representative projects include mobile web performance optimization, new features in Android to greatly reduce network data usage and energy consumption; new platforms for developing high performance web applications on mobile devices; wireless communication protocols that will yield vastly greater performance over today’s standards; and multi-device interaction based on Android, which is now available on a wide variety of consumer electronics.

Recent Publications

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UE Security Reloaded: Developing a 5G Standalone User-Side Security Testing Framework
Aanjhan Ranganathan
Christina Pöpper
Evangelos Bitsikas
Roger Piqueras Jover
Syed Khandker
16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (2023)
Preview abstract Security flaws and vulnerabilities in cellular networks directly lead to severe security threats given the data-plane services, from calls to messaging and Internet access, that are involved. While the 5G Standalone (SA) system is currently being deployed worldwide, practical security testing of user equipment has only been conducted for 4G/LTE and earlier network generations. In this paper, we develop and present the first security testing framework for 5G SA user equipment. To that end, we modify the functionality of open-source suites (Open5GS and srsRAN) and develop a broad set of test cases for 5G NAS and RRC layers. We apply our testing framework in a proof-of-concept manner to 5G SA mobile phones, report identified vulnerabilities, and provide detailed insights from our experiments. View details
The impact of open-source on cellular network security
Roger Piqueras Jover
(2023)
Preview abstract Keynote at the srsRAN Project Workshop in October 2023: https://srs.io/srsran-project-workshop-october-23-24/ The talk is a summary of the impact that open-source tools and SW radio have had on cellular security research in academia over the last 15 years. It summarizes 2G security research in ~2008-2012 and how the first OSS tools for LTE (openLTE and srsLTE) were a game changer for the field, enabling a tremendous spike in excellent cellular security research work. View details
APG: Audioplethysmography for Cardiac Monitoring in Hearables
Xiaoran Fan
David Pearl
Rich Howard
Longfei Shangguan
Trausti Thormundsson
MobiCom 2023: The 29th Annual International Conference On Mobile Computing And Networking (MobiCom), Association for Computing Machinery (ACM) (to appear)
Preview abstract This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones. APG sends a low intensity ultrasound probing signal using an ANC headphone's speakers and receives the echoes via the on-board feedback microphones. We observed that, as the volume of ear canals slightly changes with blood vessel deformations, the heartbeats will modulate these ultrasound echoes. We built mathematical models to analyze the underlying physics and propose a multi-tone APG signal processing pipeline to derive the heart rate and heart rate variability in both constrained and unconstrained settings. APG enables robust monitoring of cardiac activities using mass-market ANC headphones in the presence of music playback and body motion such as running. We conducted an eight-month field study with 153 participants to evaluate APG in various conditions. Our studies conform to the (Institutional Review Board) IRB policies from our company. The presented technology, experimental design, and results have been reviewed and further improved by feedback garnered from our internal Health Team, Product Team, User Experience (UX) Team and Legal team. Our results demonstrate that APG achieves consistently high HR (3.21% median error across 153 participants in all scenarios) and HRV (2.70% median error in interbeat interval, IBI) measurement accuracy. Our UX study further shows that APG is resilient to variation in: skin tone, sub-optimal seal conditions, and ear canal size. View details
Design Earable Sensing Systems: Perspectives and Lessons Learned from Industry
Xiaoran Fan
Trausti Thormundsson
Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computers, ACM (to appear)
Preview abstract Earables computing is an emerging research community as the industry witnesses the soaring of True Wireless Stereo (TWS) Active Noise Canceling (ANC) earbuds in the past ten years. There is an increasing trend of newly initiated earable research spanning across mobile health, user-interfaces, speech processing, and context-awareness. Head-worn devices are anticipated to be the next generation Mobile Computing and Human-Computer Interaction (HCI) platform. In this paper, we share our design experiences and lessons learned in building hearable sensing systems from the industry perspective. We also give our takes on future directions of the earable research. View details
Spatial model personalization in Gboard
Gary Sivek
Michael D. Riley
Preview abstract We introduce a framework for adapting a virtual keyboard to individual user behavior by modifying a Gaussian spatial model to use personalized key center offset means and, optionally, learned covariances. Through numerous real-world studies, we determine the importance of training data quantity and weights, as well as the number of clusters into which to group keys to avoid overfitting. While past research has shown potential of this technique using artificially-simple virtual keyboards and games or fixed typing prompts, we demonstrate effectiveness using the highly-tuned Gboard app with a representative set of users and their real typing behaviors. Across a variety of top languages,we achieve small-but-significant improvements in both typing speed and decoder accuracy. View details
Mobile Applications On-Device Testing at Google scale
Denis Krupennikov
William Hester
Diana Cortes
Chris Cole
N/A, N/A (2022), N/A
Preview abstract Over the years, the mobile devices landscape has grown to hundreds of OEMs and tens of thousands of device models. This landscape has made it difficult to develop quality mobile applications that are user-friendly, stable, and performant. To accelerate mobile development and to help developers build better performing, more stable apps, Google built a large Mobile Device Farm that allows developers to test their mobile applications. In this document we share lessons learned while building the Mobile Device Farm, including pitfalls and successes, to help the mobile development community leverage our experience and build better mobile apps. While we describe both Android and iOS, we primarily focus on the Android Open Source Project (AOSP) because it is highly diverse and dynamic. We've scaled from 10s of devices to 10s of thousands, and from hundreds of tests a day to millions. View details

Some of our teams