The quantitative overhead analysis for effective task migration in biosensor networks.
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 computin...
| Publicado en: | BioMed Research International Vol. 2013; pp. 965318 - 965319 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104110477&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104110477 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104110477 104110477 2012357486 NLM24187668 PMC3804045 104110477 ppf: 965318 ppct: 1 formats: fmt: @attributes: type: P tig: atl: The quantitative overhead analysis for effective task migration in biosensor networks. aug: au: Jung, Sung-Min Kim, Tae-Kyung Eom, Jung-Ho Chung, Tai-Myoung affil: Department of Electrical and Computer Engineering, Sungkyunkwan University, 300 Cheoncheon-dong, Jangan-gu, Suwon-si, Gyeonggi-do 440-746, Republic of Korea. sug: subj: Algorithms Biosensing Techniques Equipment and Supplies Computer Communication Networks Equipment and Supplies Monitoring, Physiologic Equipment and Supplies Bar Coding Biosensing Techniques Statistics and Numerical Data Computer Communication Networks Computer Simulation Human Internet Monitoring, Physiologic Statistics and Numerical Data Time Factors ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|