On Open Source Mobile
Dmitry Namiot Lomonosov Moscow State University
Manfred Sneps-Sneppe ZNIIS, M2M Competence Center
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.
On challenges for mobile phone sensing
Open source libraries for mobile phone sensing
The model and patterns
• Rich sensing capabilities for smart phones
• Collecting data about people’s social behavior
(computational social science – e.g., Reality
• Crowd-sensing for business-related tasks (e.g.
• Balance between energy efficiency, data
collection, storage, and transmission procedures
• Batteries as a major challenge in achieving
• Sensors power consumption: GPS vs.
• 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
Challenges for Open Source
• Context-aware data collecting. How to
reduce measurements and data
• A flexible data management. SD-card vs.
• Portable (common) data formats
• Built-in data processing
API vs. DPI
• Traditionally: mobile OS presents API for
• APIs used by mobile applications
• The standard approach for crowd-sensing
is to split data collecting and data
• So, we have to switch to DPI – Data
• 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
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,
M2M applications, context-aware computing.