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Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications
- Source :
- IEEE Transactions on Human-Machine Systems; January 2013, Vol. 43 Issue: 1 p115-133, 19p
- Publication Year :
- 2013
-
Abstract
- Wireless body sensor networks (BSNs) possess enormous potential for changing people's daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed.
Details
- Language :
- English
- ISSN :
- 21682291
- Volume :
- 43
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Human-Machine Systems
- Publication Type :
- Periodical
- Accession number :
- ejs29366191
- Full Text :
- https://doi.org/10.1109/TSMCC.2012.2215852