Abstract
Sensor networks are data-centric networks constrained with limited battery power and processing capabilities. One of the crucial challenges in sensor network is energy hole problem. In order to deal with the challenge, there exists several mechanisms, of which clustering is considered an energy-efficient solution. In general, clustering refers to the technique of grouping nodes on the basis of similarity in spatial arrangement. An appropriately clustered network helps in processing and aggregation of sensed data before routing the information to destined location. This paper proposes a soft computing based recursive approach for implementing density-based hierarchical clustering, for Gaussian distributed sensor network. Our proposed probabilistic approach creates hierarchical clusters recursively, which not only addresses the problem of energy hole, but also reduces transmission delay, thereby maintaining data freshness.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14–15):2826–2841
Bryant AC, Cios KJ (2018) RNN-DBSCAN: a density-based clustering algorithm using reverse nearest neighbor density estimates. IEEE Trans Knowl Data Eng 30(6):1109–1121
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference knowledge discovery and data mining (KDD), Aug 1996, vol 96, no 34, pp 226–231
Gulati H, Singh PK (2015) Clustering techniques in data mining: a comparison. In: 2nd international conference on computing for sustainable global development (INDIACom), Mar 2015. IEEE, pp 410–415
Hinneburg A, Keim DA (1998) An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the 4th international conference knowledge discovery and data mining (KDD), Aug 1998, pp 58–65
Hyde R, Angelov P (2014a) Data density based clustering. In: 14th UK workshop on computational intelligence (UKCI), Sept 2014. IEEE, pp 1–7
Hyde R, Angelov P (2014b) A fully autonomous data density based clustering technique. In: IEEE symposium on evolving and autonomous learning systems (EALS), Dec 2014. IEEE, pp 116–123
Jain A, Murty M, Flynn P (1999) Data clustering: A review. ACM Comput Survey 31(3):264–323
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666
Matin MA, Islam MM (2012) Overview of wireless sensor. Networks.https://doi.org/10.5772/49376ISBN978-953-51-0735-4
Mihael Ankerst , Markus M. Breunig , Hans-Peter Kriegel , Jörg Sander (1999) OPTICS: ordering points to identify the clustering structure, Proceedings of the ACM SIGMOD international conference on Management of data, p.49–60, May 31-June 03, , Philadelphia, Pennsylvania, USA
Plamen, Parvanov, Angelov et al. (2016), Autonomous data density-based clustering method. In: The bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI). The bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI), pp. 2405-2413.
Rahman A, Chowdhury AR, Rahman DJ, Kamal ARM (2008) Density based clustering technique for efficient data mining. In: 11th international conference on computer and information technology, Dec 2008. IEEE, pp 248–252
Sander J, Ester M, Kriegel H-P, Xiaowei Xu (1998) Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min Knowl Disc 2(2):169–194
Sirsikar S, Anavatti S (2015) Issues of data aggregation methods in wireless sensor network: a survey. Elsevier Procedia Comput Sci 49:194–201
Toor AA , Usman M, Ahmed W (2016) ,A critical review of density-based data stream clustering techniques, Digital Information Management (ICDIM), In Eleventh International Conference, pp. 51-61
Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16:645–678
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gupta, M., Sinha, A. Recursive density-based hierarchical clustering in gaussian distributed sensor network. Int J Syst Assur Eng Manag 14, 19–28 (2023). https://doi.org/10.1007/s13198-020-01009-3
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-020-01009-3