Abstract
The integration of Wireless sensor networks (WSN) and Internet of Things (IoT) perform many tasks control or monitor the surrounding area or the environment. The WSN-based IoT consists of many sensor nodes connect which transmit the collecting data of the environment to the manager through the Internet. The network topology requires high reliability connections while requires low energy consumption at the sink node and long network lifetime. In this paper, we introduce the self-learning clustering protocol to discover neighbors and the network topology. The cluster head is selected based on the information of the neighbors and the residual energy of the node. The maximum number of cluster members is set according to the network density. The proposed protocol can adapt the changing of the dynamic network with low energy consumption; therefore, ensuring the network connectivity. The simulation results show that the proposed clustering protocol performs well in terms of long network lifetime and high throughput while comparing to other clustering protocols.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ghayvat, H., Mukhopadhyay, S., Gui, X., Suryadevara, N.: WSN- and IoT-based smart homes and their extension to smart buildings. Sensors 15(5), 10350–10379 (2015)
Khalil, N., Abid, M.R., Benhaddou, D., Gerndt, M.: Wireless sensors networks for Internet of Things. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6. IEEE, April, 2014
Bajaj, K., Sharma, B., Singh, R.: Integration of WSN with IoT applications: a vision, architecture, and future challenges. In: Rani, S., Maheswar, R., Kanagachidambaresan, G.R., Jayarajan, P. (eds.) Integration of WSN and IoT for Smart Cities. EICC, pp. 79–102. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38516-3_5
Sharma, H., Haque, A., Blaabjerg, F.: Machine learning in wireless sensor networks for smart cities: a survey. Electronics 10(9), 1012 (2021)
Bensaid, R., Said, M.B., Boujemaa, H.: Fuzzy C-means based clustering algorithm in WSNs for IoT applications. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 126–130. IEEE (2020)
Asiri, M., Sheltami, T., Al-Awami, L., Yasar, A.: A Novel approach for efficient management of data lifespan of IoT devices. IEEE Internet Things J. 7(5), 4566–4574 (2019)
Heinzelman, W., Chandrakasan, A., Balakrishnan, H: Efficient routing protocols for wireless microsensor networks. In: Proceedings of the 33rd Hawaii International on Conference System Sciences (HICSS), USA, 7 Jan 2000, p. 10. (2000)
Nguyen, N., Ho, C.V., Le, T.T.T.: A topology control algorithm in wireless sensor networks for IoT-based applications. In: 2019 International Symposium on Electrical and Electronics Engineering (ISEE), pp. 141–145 (2019). https://doi.org/10.1109/ISEE2.2019.8921357
Alharbi, M.A., Kolberg, M., Zeeshan, M.: Towards improved clustering and routing protocol for wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2021(1), 1–31 (2021). https://doi.org/10.1186/s13638-021-01911-9
Acknowledgments
The authors would like to thank the anonymous reviewers for the helpful comments and suggestions. This research was supported by the Ministry of Education, Youth and Sports of the Czech Republic under the grant SP2021/25 and e-INFRA CZ (ID:90140). Correspondence should be addressed to Nhat Tien Nguyen (tien.nn@sgu.edu.vn).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, N.T., Le, T.T.T., Voznak, M., Zdralek, J. (2022). A Self-learning Clustering Protocol in Wireless Sensor Networks for IoT Applications. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-84910-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84909-2
Online ISBN: 978-3-030-84910-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)