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MobiData '16: Proceedings of the First Workshop on Mobile Data
ACM2016 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services Singapore Singapore 30 June 2016
ISBN:
978-1-4503-4327-5
Published:
30 June 2016
Sponsors:
In-Cooperation:
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Abstract

It is our great pleasure to welcome you to the 1st ACM MobiSys Workshop on Mobile Data (MobiData 2016). In this first edition, we aimed to attract early works in use of mobile data for novel applications. The Internet has been radically transformed since the introduction of smartphones in the last few years. The increasing processing power, connectivity modes, variety of sensors and features, and the personal, always-on nature of these devices have all led to an immense growth in the smartphone and data industry. With millions of apps that users can download, each able to access rich data about users via their smartphones, we are entering an unprecedented era of technology driven by personal and environmental data.

A key factor driving growth in the mobile industry is the ability to infer rich data from individuals, perform behavioural analytics, provide personalized services, and/or deliver targeted ads. The capabilities and features of modern mobile technologies and the associated benefits and opportunities are not well explored yet. Mobile applications and services come with a range of technical, legal, societal and ethical challenges unseen in previous computing paradigms and networked systems. These range from excessive usage of bandwidth and power, to aggressive collection, management and careless share of personal information. Long term and sustainable growth in this space is dependable on the ability of the research, industry, and the developer community to address these issues with the user in mind.

In this workshop we will bring together academic researchers and industry practitioners to present their latest research in the space of mobile data. We will aim to provide a forum for discussing early work, novel approaches, and controversial ideas in use of mobile data and systems and design of technologies that address the mentioned challenges.

We also encourage attendees to attend the keynote presentation by Professor David Kotz:

  • Security & Privacy for Healthcare Information Systems, David Kotz, Champion International Professor Department of Computer Science, Dartmouth College

We hope that you will find this workshop interesting and thought-provoking.

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SESSION: Session 1A
research-article
The Wireless Shark: Identifying WiFi Devices Based on Probe Fingerprints

Due to the broadcast nature of WiFi communication anyone with suitable hardware is able to monitor surrounding traffic. However, a WiFi device is able to listen to only one channel at any given time. The simple solution for capturing traffic across ...

research-article
The Need to Account for Geographical Diversities in Mobile Data Research

Large-scale mobile data studies have the potential to provide valuable insights regarding usage behavior of smartphone users. A major challenge in generalising the findings of these studies is the inherent population diversity in large-scale smartphone ...

SESSION: Session 1B
research-article
Public Access
Mining Spatial-Temporal geoMobile Data via Feature Distributional Similarity Graph

Mobile devices and networks produce abundant data that exhibit geo-spatial and temporal properties mainly driven by human behavior and activities. We refer to such data as geoMobile data. Mining such data to extract meaningful patterns that are ...

research-article
Indoor Location Error-Detection via Crowdsourced Multi-Dimensional Mobile Data

We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi ...

research-article
Oscillation Resolution for Massive Cell Phone Traffic Data

Cellular towers capture logs of mobile subscribers whenever their devices connect to the network. When the logs show data traffic at a cell tower generated by a device, it reveals that this device is close to the tower. The logs can then be used to ...

Contributors
  • Northeastern University
  • Brave Software, Inc.
  • IMDEA Networks Institute

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