On open source mobile sensing

Our presentation for ruSMART 2014:

Namiot, D., & Sneps-Sneppe, M. (2014). On Open Source Mobile Sensing. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems (pp. 82-94). Springer International Publishing.

A real machine learning

Understand the business problem - a good article about the solving real problems with machine learning.

Network proximity on web

This paper is devoted to context-aware computing. As the basic information about the context, we use information about surrounding wireless networks (Wi-Fi access points, Bluetooth nodes). The aim is to consider the use of this kind of information about network proximity directly in the mobile web applications.

Please, support our presentation

IoT standards

6 emerging standards for IoT. No chances to win with so many "standards" ;-)

INJOIT - call for papers

The International Journal of Open Information Technologies (INJOIT) is an all-electronic journal with the aim to bring the most recent and unpublished research and development results in the area of information technologies to the scientific and technical societies. Free, peer reviewed papers. English or Russian languages.

It is published by the OIT Lab (Open Information Technologies Lab, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University).

Indexing: Google Scholar, DOAJ, Ulrich's Periodicals Directory, ResearchBib, Elibrary.ru (RINC)

Signal processing

A couple of the useful and free (!) books:

Introduction to Signal Processing

Scientist and Engineer's Guide to Digital Signal Processing

M2M Software Platforms

Our new paper: Namiot, D., & Sneps-Sneppe, M. (2014). On M2M Software Platforms. International Journal of Open Information Technologies, 2(8), 29-33.

This paper provides an overview for existing and upcoming system software platforms for M2M applications. In this article we discuss system software models from the developer’s point of view, rather than network related aspects. The primary goal is to find the common and reusable aspects across existing models as well as discuss their possible coexistence. Can we extract the common elements for the different M2M software models? Are there some reusable patterns? What should developers and system architects pay attention to? These are the main issues addressed in this article.

P.S. See also the previous paper: Namiot, Dmitry, and Manfred Sneps-Sneppe. "On M2M Software." International Journal of Open Information Technologies 2.6 (2014): 29-36

Open Source Mobile Sensing

Our new paper: Namiot, Dmitry, and Manfred Sneps-Sneppe. "On Open Source Mobile Sensing." Internet of Things, Smart Spaces, and Next Generation Networks and Systems. Springer International Publishing, 2014. 82-94.

The paper discusses phone as a sensor model. For many applied tasks smart phones are an ideal platform for collecting and processing context-related data. The most popular example is, probably, computational social science. Phones can collect data for conducting various social researches about people’s social behavior. This paper presents an attempt to describe and categorize existing open source libraries for mobile sensing, describe architecture and design patterns as well as discover directions for the future development.

Geo Messages in iOS

Our idea about Geo Messages (geo-signatures for messages) is getting alive with iOS8:

/via GPS Business News

BDP landing page

An interesting idea - Appsites service creates landing pages for mobile applications based on the data in Google Play (App Store).

For example, we can take our BDP application, points service to its page in Google Play and automatically get this landing page for our application: Bluetooth Data Points