Sensing

964 views

Published on

Our presentation on ruSMART 2014 about open source mobile sensing

Published in: Software
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
964
On SlideShare
0
From Embeds
0
Number of Embeds
422
Actions
Shares
0
Downloads
8
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Sensing

  1. 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. 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. 3. Contents Introduction On challenges for mobile phone sensing Open source libraries for mobile phone sensing The model and patterns Conclusion
  4. 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. 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. 6. External collections
  7. 7. Open Source Frameworks • AWARE framework: client + server
  8. 8. Open Source Frameworks • FUNF framework
  9. 9. Open Source Frameworks • Open Data Kit
  10. 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. 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. 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. 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.

×