Key Nodes in Wireless Sensor Networks: Centrality Measures and Principal Component Analysis in Watts-Strogatz, Random Walk, and Barabási-Albert Models
Key Nodes in Wireless Sensor Networks: Centrality Measures and Principal Component Analysis in Watts-Strogatz, Random Walk, and Barabási-Albert Models |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-12 |
||
| Year of Publication : 2024 | ||
| Author : Suneela Kallakunta, Alluri Sreenivas |
||
| DOI : 10.14445/22315381/IJETT-V72I12P103 | ||
How to Cite?
Suneela Kallakunta, Alluri Sreenivas, "Key Nodes in Wireless Sensor Networks: Centrality Measures and Principal Component Analysis in Watts-Strogatz, Random Walk, and Barabási-Albert Models," International Journal of Engineering Trends and Technology, vol. 72, no. 12, pp. 30-41, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I12P103
Abstract
Wireless Sensor Networks (WSNs) have facilitated the advancement of communication systems. WSNs are deployed in sectors where metrics and performance factors are calculated mainly based on particular nodes, the importance of which should be emphasized. Centrality measures are enumerated to include Degree, Betweenness, Closeness, Eigenvector, Katz and Subgraph Centralities, which directly assess the importance of individual network nodes. The research explicitly considers the generation of WSNs with 150 nodes using the Watts-Strogatz, Random Walk and Barabasi-Albert models, paying attention to essential nodes that affect the network level and its features. Using Pearson correlation analysis, Kendall rank correlation analysis and Spearman rank correlation analysis determine how centrality measures correlate in each model. Principal Component Analysis (PCA) determines how many nodes need to be used to determine the centrality data to contain the most variance across the different models. The findings in this study also underscore the importance of the centrality measures in explaining the network's topology and make it clear why some nodes are more important than others; the information provided may assist in creating more efficient WSN structures.
Keywords
Wireless Sensor Networks (WSNs), Centrality measures, Key node identification, Principal component analysis (PCA), Network optimization.
References
[1] I.F. Akyildiz et al., “A Survey on Sensor Networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102-114, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jennifer Yick, Biswanath Mukherjee, and Dipak Ghosa, “Wireless Sensor Network Survey,” Computer Networks, vol. 52, no. 12, pp. 2292-2330, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Linton C. Freeman, “A Set of Measures of Centrality Based on Betweenness,” Sociometry, vol. 40, no. 1, pp. 35-41, 1977.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Phillip Bonacich, “Power and Centrality: A Family of Measures,” American Journal of Sociology, vol. 92, no. 5, pp. 1170-1182, 1987.
[CrossRef] [Google Scholar] [Publisher Link]
[5] M.E. J. Newman, “A Measure of Betweenness Centrality Based on Random Walks,” Social Networks, vol. 27, no. 1, pp. 39-54, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ulrik Brandes, “A Faster Algorithm for Betweenness Centrality,” Journal of Mathematical Sociology, vol. 25, no. 2, pp. 163-177, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Masood Alam Abbasi, “Realization of centrality measure on Wireless Sensor Network,” In Proceedings of the 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Karachi, Pakistan, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Ameer Ahmed Abbasi, and Mohamed Younis, “A Survey on Clustering Algorithms for Wireless Sensor Networks,” Computer Communications, vol. 30, no. 14-15, pp. 2826-2841, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Duncan J. Watts, and Steven H. Strogatz., “Collective Dynamics of ‘Small-World’ Networks,” Nature, vol. 393, no. 6684, pp. 440-442, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[10] László Lovasz, “Random Walks on Graphs: A Survey,” Combinatorics, Paul Erdős also Eighty, vol. 2, pp. 1-46, 1993.
[Google Scholar] [Publisher Link]
[11] Asaf Nachmias, Random Walks and Electric Networks, Lecture Notes in Mathematics, Springer, Cham, vol. 2243, pp. 11-31, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Albert-László Barabási, and Réka Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no. 5439, pp. 509-512, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Réka Albert, Hawoong Jeong, and Albert-László Barabási, “Error and Attack Tolerance of Complex Networks,” Nature, vol. 406, no. 6794, pp. 378-382, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Jan Hauke, and Tomasz Kossowski, “Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data,” Geographical questions, vol. 30, no. 2, pp. 87-93, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. G. Kendall, “A New Measure of Rank Correlation,” Biometrika, vol. 30, no. 1-2, pp. 81-93, 1938.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Charles Spearman, “The Proof and Measurement of Association Between Two Things,” The American Journal of Psychology, vol. 15, no. 1, pp. 72-101, 1904.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ian T. Jolliffe, and Jorge Cadima, “Principal Component Analysis: A Review and Recent Developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, pp. 1-16, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hervé Abdi, and Lynne J. Williams, “Principal Component Analysis,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433-459, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Meimei Ding, and Hui Tian, “PCA-Based Network Traffic Anomaly Detection,” Tsinghua Science and Technology, vol. 21, no. 5, pp. 500-509, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yihua Liao, and V. Rao Vemuri, “Use of K-Nearest Neighbor Classifier for Intrusion Detection,” Computers & Security, vol. 21, no. 5, pp. 439-448, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shancang Li, Li Da Xu, and Shanshan Zhao, “The Internet of Things: A Survey,” Information Systems Frontiers, vol. 17, no. 2, pp. 243-259, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Samuel Kofi Erskine, “Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks,” Sensors, vol. 24, no. 7, pp. 2090, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Omar Banimelhem, Eyad Taqieddin, and Ibrahim Shatnawi, “An Efficient Path Generation Algorithm Using Principal Component Analysis for Mobile Sinks in Wireless Sensor Networks,” Journal of Sensor and Actuator Networks, vol. 10, no. 4, pp. 1-22, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mark Newman, Networks, 2nd ed., Oxford University Press, United States, 2018.
[Google Scholar] [Publisher Link]
[25] Tore Opsahl, Filip Agneessens, and John Skvoretz, “Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths,” Social Networks, vol. 32, no. 3, pp. 245-251, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Phillip Bonacich, “Power and Centrality: A Family of Measures,” American Journal of Sociology, vol. 92, no. 5, pp. 1170-1182, 1987.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mark Newman, Networks: An Introduction, Oxford University Press, 2010.
[CrossRef] [Publisher Link]
[28] Leo Katz, “A New Status Index Derived from Sociometric Analysis,” Psychometrika, vol. 18, no. 1, pp. 39-43, 1953.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Ernesto Estrada, and Juan A. Rodríguez-Velázquez, “Subgraph Centrality in Complex Networks,” Physical Review E, vol. 71, no. 5, 2005.
[CrossRef] [Google Scholar] [Publisher Link]