Abstract
Information collection and target tracking are key technologies for wireless sensor network applications. Signal interference will cause the node to frequently jitter between cluster members and independent nodes. In order to improve the signal transmission efficiency, this study analyzed the current common sensor networks and theoretically analyzed and optimized various parameters. At the same time, based on the behavior characteristics, this paper proposed a mobile phone algorithm for positioning information. In addition, this paper used a probability-based state notification mechanism to perform cluster maintenance. Finally, the experiment was designed to analyze the performance of the proposed algorithm. Research shows that the proposed algorithm has certain effects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cheung,Y.M., Zhang, Y.: Fast and accurate hierarchical clustering based on growing multilayer topology training. IEEE Trans. Neural Netw. Learn. Syst. 1–15 (2018)
Chen, J., Zheng, H., Lin, X., et al.: A novel image segmentation method based on fast density clustering algorithm. Eng. Appl. Artif. Intell. 73, 92–110 (2018)
Wu, D., Ren, J., Sheng, L.: Representative points clustering algorithm based on density factor and relevant degree. Int. J. Mach. Learn. Cybern. 8(2), 641–649 (2017)
Lei, T., Jia, X., Zhang, Y., et al.: Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans. Fuzzy Syst. 26(5), 3027–3041 (2018)
Huang, D., Wang, C.D., Peng, H., et al.: Enhanced ensemble clustering via fast propagation of cluster-wise similarities. IEEE Trans. Syst. Man Cybern. Syst. 1–13 (2018)
Fernandes, C.M., Mora, A.M., Merelo, J.J., et al.: KANTS: a stigmergic ant algorithm for cluster analysis and swarm art. IEEE Trans. Cybern. 44(6), 843–856 (2014)
Ge, H., Sun, L., Yu, J.: Fast batch searching for protein homology based on compression and clustering. BMC Bioinf. 18(1), 508 (2017)
Park, S., Choi, H., Lee, B., et al.: hc-OTU: a fast and accurate method for clustering operational taxonomic unit based on homopolymer compaction. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(2), 441–451 (2018)
Unsupervised hyperspectral remote sensing image clustering based on adaptive density. IEEE Geosci. Remote Sens. Lett. 15(4), 632–636 (2018)
Sun, L., Guo, C., Liu, C., et al.: Fast affinity propagation clustering based on incomplete similarity matrix. Knowl. Inf. Syst. 51(3), 941–963 (2017)
Acknowledgement
1. Ministry of Education Industry-University Cooperation Collaborative Education Project (Project No.: 201802314001).
2. Shaanxi Provincial Science and Technology Department Project (Project No.: 2017JM6036).
3. Electronic Information (Computer Technology) Master’s Degree Construction Project (Project No.: 18TSXK06).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tian, X., Sun, X. (2020). Multi-cluster Fast Information Statistics Algorithm Based on Probability. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_250
Download citation
DOI: https://doi.org/10.1007/978-3-030-25128-4_250
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25127-7
Online ISBN: 978-3-030-25128-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)