Artificial immune system based approach to fault diagnosis for wireless sensor networks

Yongjun Chen; Shenfang Yuan

doi:10.1117/12.840030

20 October 2009 Artificial immune system based approach to fault diagnosis for wireless sensor networks

Yongjun Chen, Shenfang Yuan

Proceedings Volume 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering; 749330 (2009) https://doi.org/10.1117/12.840030
Event: Second International Conference on Smart Materials and Nanotechnology in Engineering, 2009, Weihai, China

Abstract

Fault diagnosis has been recognized as one of the key issues in wireless sensor networks. Considering distribution feature of sensor node, however, the fault happened in wireless sensor networks is usually random and unpredictable. The conventional diagnosis approaches become increasingly difficult to deal with. As a result, the application is limited seriously. To solve the problem, a new approach based on artificial immune system for fault diagnosis is proposed. The normal and abnormal character patterns generated by a network simulator for wireless sensor networks, respectively, are regarded as the self and antigen of artificial immune system. According to a real-valued negative selection algorithm, the detectors are generated to improve the covering ability of non-self space. Taking detector as antibody, an immunity calculation is executed by the distribution zones of antibody and evolution learning mechanism of artificial immune system. The type of antigen is decided based on the clustering distribution of cloned and matured antibody. The example shows that the approach has better accuracy and the capability of self-adaptive for the fault diagnosis in wireless sensor networks.

Citation Download Citation

Yongjun Chen and Shenfang Yuan "Artificial immune system based approach to fault diagnosis for wireless sensor networks", Proc. SPIE 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering, 749330 (20 October 2009); https://doi.org/10.1117/12.840030

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available