Abstract
This work is considered a clustering problem in a wireless sensor network, where sensor nodes are artificially generated and randomly distributed over the range of the network. The sensor is small in size, a short distance in communication, limited in storage space, and un-rechargeable battery. The task of this sensor is to sense the data from the area of being deployed. The clustering technique is employed to partition the area of the application into sub-areas; the distance-based method is used to partition the sensors in WSN. In this paper, we propose K-means-based method for clustering and validating grouping of sensor nodes by using external indices named purity. The proposed method can solve the clustering problem in WSN by partitioning the provided artificial sensor set into sub-clusters and validate them. The simulation result shows the ability of the proposed method in solving the power consumption by dividing the region of sensing and confirms that this method is suitable for large-scale wireless networks.
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References
Qin J, Fu W, Gao H, Zheng WX (2016) Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory. IEEE Trans Cybern Includes Comput Approaches Field Cybern 47(3):1–12
Jain B, Brar G, Malhotra J (2018) EKMT-k-means clustering algorithmic solution for low energy consumption for wireless sensor networks based on minimum mean distance from base station. Network Commun Data Knowl Eng 1:113–123
Ke W, Yangrui O, Hong J, Heli Z, Xi L (2016) Energy aware hierarchical cluster-based routing protocol for WSNs. Posts Telecommun 23(4):46–52
Nasser AMT, Pawar VP (2015) Machine learning approach for sensors validation and clustering. In: International conference on emerging research in electronics, computer science and technology (ICERECT), pp 370–375
Ray A, De D (2016) Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network. IET Wirel Sens Syst 6(6):181–191
Singh S, Chand S, Kumar R, Malik A, Kumar B (2016) NEECP: novel energy-efficient clustering protocol for prolonging lifetime of WSNs. IET Wirel Sens Syst 6(6):151–157
Sasikumar P, Khara S (2012) k-means clustering in wireless sensor networks. In: International conference on computational intelligence and communication networks, pp 140–144
Bhavsar M, Patel N, Raval G (2017) Enhancing data delivery with density controlled clustering in wireless sensor networks. Microsyst Technol 23(3):613–631
Arghavani M, Esmaeili M, Esmaeili M, Mohseni F (2017) Optimal energy aware clustering in circular wireless sensor networks. Ad Hoc Netw 65:91–98
Oladimeji MO, Turkey M, Dudley S (2017) HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks. Appl Soft Comput 55:452–461
Liu Y, Li Z, Xiong H, Gao X, Wu J, Wu S (2013) Understanding and enhancement of internal clustering validation measures. IEEE Trans Cybern 43(3):982–994
Maulik U, Bandyopadhyay S (2002) Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell 24(12):1650–1654
Liu Y, Li Z, Xiong H, Gao X, Wu J (2016) Understanding of internal clustering validation measures. In: IEEE international conference on data mining (ICDM), pp 911–916
Kim M, Ramakrishna RS (2005) New indices for cluster validity assessment. Pattern Recogn Lett 26(15):2353–2363
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Almajidi, A.M., Pawar, V.P., Alammari, A. (2019). K-Means-Based Method for Clustering and Validating Wireless Sensor Network. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-13-2324-9_25
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DOI: https://doi.org/10.1007/978-981-13-2324-9_25
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