This document discusses open source mobile sensing. It provides an overview of existing open source libraries for mobile sensing and categorizes them. It also examines challenges in mobile sensing, such as battery life and data collection procedures. The goal is to describe current architectures and designs and identify future directions for open source mobile sensing frameworks.

1. On Open Source Mobile
Sensing
Dmitry Namiot Lomonosov Moscow State University
dnamiot@gmail.com
Manfred Sneps-Sneppe ZNIIS, M2M Competence Center
manfreds.sneps@gmail.com
ruSMART 2014

2. About
Phone as a sensor model.
• Smart phones as an ideal platform for collecting
and processing context-related data.
• Computational social science, crowdsensing
• An an attempt to describe and categorize existing
open source libraries for mobile sensing,
• Describe architecture and design patterns
• Discover directions for the future development.

3. Contents
Introduction
On challenges for mobile phone sensing
Open source libraries for mobile phone sensing
The model and patterns
Conclusion

4. Introduction
• Rich sensing capabilities for smart phones
• Collecting data about people’s social behavior
(computational social science – e.g., Reality
Mining)
• Crowd-sensing for business-related tasks (e.g.
OpenSignal)
• Balance between energy efficiency, data
collection, storage, and transmission procedures

5. Challenges
• Batteries as a major challenge in achieving
social sensing
• Sensors power consumption: GPS vs.
accelerometer
• Context-aware data collecting. E.g. reuse
location for phone on the table, SD card vs.
cloud storage, etc.
• High level of diversification in mobile sensors

6. External collections

7. Open Source Frameworks
• AWARE framework: client + server

8. Open Source Frameworks
• FUNF framework

9. Open Source Frameworks
• Open Data Kit

10. Challenges for Open Source
Frameworks.
• Context-aware data collecting. How to
reduce measurements and data
transmission
• A flexible data management. SD-card vs.
Cloud
• Portable (common) data formats
• Built-in data processing

11. API vs. DPI
• Traditionally: mobile OS presents API for
built-in sensors
• APIs used by mobile applications
• The standard approach for crowd-sensing
is to split data collecting and data
processing
• So, we have to switch to DPI – Data
Programming Interfaces

12. Conclusion
• A survey of the Open Source tools
for mobile sensing.
• Existing projects
• Directions for the future research
• The prediction: we will see mobile sensing as a part
of mobile OS
• An existing example: iBeacons в iOS

13. About us
International team: Russia - Latvia (Moscow –
Riga – Ventspils). Big history of developing
innovative telecom and software services,
international contests awards
Research areas are:
open API for telecom,
web access for telecom data,
Smart Cities,
M2M applications, context-aware computing.