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Centrality Measure for Quantitative Estimation of Node Criticality in a Giant Connected Component of a Wireless Sensor Network
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ki-Hyung Kim | - |
| dc.contributor.author | MANSOOR, Usman | - |
| dc.date.accessioned | 2018-11-08T07:58:29Z | - |
| dc.date.available | 2018-11-08T07:58:29Z | - |
| dc.date.issued | 2013-02 | - |
| dc.identifier.other | 13924 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/9251 | - |
| dc.description | 학위논문(석사)아주대학교 교육대학원 :컴퓨터교육,2013. 2 | - |
| dc.description.tableofcontents | Acknowledgements iv 1. Introduction 1 2. Related work 3 3. Centrality Measures 5 3.1 Network Definition 5 3.2 Geodesic Information Based 6 3.2.1 Closeness Centrality 6 3.2.2 Betweeness Centrality 6 3.2.3 Bridging Centrality 7 3.3 Degree Based 7 3.3.1 Degree Centrality 7 3.3.2 Spectral Centrality 8 4. Centrality Based Network Study 8 4.1 Experiment Setup 8 4.2 Centralities Analysis 9 5. Centrality Measure for WSNs 16 5.1 Essential Terminologies 16 5.2 Centrality Formulation 17 6. The Trends: Criticality Factor (CF) 23 7. Simulation results 25 8. Deployment scenario 31 9. Conclusion 36 References 37 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Centrality Measure for Quantitative Estimation of Node Criticality in a Giant Connected Component of a Wireless Sensor Network | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 교육대학원 | - |
| dc.contributor.department | 교육대학원 컴퓨터교육 | - |
| dc.date.awarded | 2013. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 570778 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000013924 | - |
| dc.description.alternativeAbstract | Load balancing in Wireless Sensor Networks is used to distribute network traffic over multiple paths. This results in uniform nodal energy dissipation and prolonged network lifetime. Most present load balancing techniques assign weights to prospective paths based on traffic patterns and hop distances, and achieve energy efficiency. However, these techniques neglect the relative criticality of a node in a giant connected component of a WSN. The relative criticality of a node in a connected network can be measured by an appropriate centrality metric such as betweenness or closeness degree. A Centrality metric measures the relative importance of a node in terms of network efficiency and utilization of the network resources. Use of centrality measures to analyze and optimize WSNs in terms of node deployment is a relatively unexplored niche. In this work, we devise a new centrality measure called Criticality Factor (CF) which is specific to WSNs and quantitatively estimates the relative criticality of a node in a cluster in terms of its upward and downward network connectivity. The CF is based on betweenness, closeness and average degree metrics. This CF can then be used in conjunction with load balancing schemes for increased energy efficiency in a WSN cluster. | - |
- Appears in Collections:
- Special Graduate Schools > Graduate School of Education > Computer Education > 3. Theses(Master)
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