Cloud-assisted body area networks: state-of-the-art and future challenges

[thumbnail of DiFatta2014-WINET-accepted.pdf]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.
| Preview

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Fortino, G., Di Fatta, G., Pathan, M. and Vasilakos, A. V. (2014) Cloud-assisted body area networks: state-of-the-art and future challenges. Wireless Networks, 20 (7). pp. 1925-1938. ISSN 1022-0038 doi: 10.1007/s11276-014-0714-1

Abstract/Summary

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/36468
Identification Number/DOI 10.1007/s11276-014-0714-1
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords Body area networks Cloud computing Mobile computing Workflow Data analysis
Publisher Springer
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar