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BioMed Research International
Volume 2013 (2013), Article ID 965318, 6 pages
http://dx.doi.org/10.1155/2013/965318
Research Article

The Quantitative Overhead Analysis for Effective Task Migration in Biosensor Networks

1Department of Electrical and Computer Engineering, Sungkyunkwan University, 300 Cheoncheon-dong, Jangan-gu, Suwon-si, Gyeonggi-do 440-746, Republic of Korea
2Department of Liberal Art, Seoul Theological University, Sosabon-dong, Sosa-gu, Bucheon-si, Gyeonggi-do 422-742, Republic of Korea
3Department of Military Studies, Daejeon University, 62 Daehakro, Dong-Gu, Daejeon-si 300-716, Republic of Korea

Received 30 June 2013; Revised 22 August 2013; Accepted 22 August 2013

Academic Editor: Tai-hoon Kim

Copyright © 2013 Sung-Min Jung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a quantitative overhead analysis for effective task migration in biosensor networks. A biosensor network is the key technology which can automatically provide accurate and specific parameters of a human in real time. Biosensor nodes are typically very small devices, so the use of computing resources is restricted. Due to the limitation of nodes, the biosensor network is vulnerable to an external attack against a system for exhausting system availability. Since biosensor nodes generally deal with sensitive and privacy data, their malfunction can bring unexpected damage to system. Therefore, we have to use a task migration process to avoid the malfunction of particular biosensor nodes. Also, it is essential to accurately analyze overhead to apply a proper migration process. In this paper, we calculated task processing time of nodes to analyze system overhead and compared the task processing time applied to a migration process and a general method. We focused on a cluster ratio and different processing time between biosensor nodes in our simulation environment. The results of performance evaluation show that task execution time is greatly influenced by a cluster ratio and different processing time of biosensor nodes. In the results, the proposed algorithm reduces total task execution time in a migration process.