Embedded self-healing layer for detecting and recovering sensor faults in body sensor networks

Galzarano, S.; Fortino, G.; Liotta, A.

Abstract

Wireless Body Sensor Networks (WBSNs) have proved to be a suitable technology for supporting the monitoring of physical and physiological activities of the human body. However, avoiding erroneous behavior of WBSN-based systems is an issue of fundamental importance, especially for critical health-care applications. In this regard, proper self-healing techniques should be able to fulfill requirements such as fault tolerance and reliability by detecting, and possibly recovering, faults and errors at runtime. In this paper, we focus on data faults, by first studying the impact of corrupted data, affecting sensed data by different kind of data-fault models, on the accuracy of a human activity recognition system. Then, we describe how the SPINE-* framework is able to enhance the WBSN system by adding instrumental autonomic elements providing the necessary self-healing operations. We find that the use of autonomic elements makes the system much more efficient and reliable thanks to its improved tolerance to data faults, as demonstrated by experimental results.

Citation

Galzarano, S., Fortino, G., & Liotta, A. (2012). Embedded self-healing layer for detecting and recovering sensor faults in body sensor networks. . https://doi.org/10.1109/ICSMC.2012.6378098

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
• Start Date: Oct 14, 2012
• End Date: Oct 17, 2012
• Online Publication Date: Dec 13, 2012
• Publication Date: Dec 13, 2012
• Deposit Date: Dec 2, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 2377-2382
• Series ISSN: 1062-922X
• DOI: https://doi.org/10.1109/ICSMC.2012.6378098
• Public URL: http://researchrepository.napier.ac.uk/Output/1995664