Development of the Multi-Channel Clustering Hierarchy Method for Increasing Performance in Wireless Sensor Network
DOI:
https://doi.org/10.30812/matrik.v23i3.3348Keywords:
Clustering Hierarchy, Multi-Channel, Performance, Sensor Network, Wireless Sensor NetworkAbstract
Wireless Sensor Networks are technologies that make it possible to observe phenomena. The problem is data delays in covering the distance from the origin to the destination. Packet Loss is a condition that shows the number of lost packets and the total queue length caused by data processing time. This research aims to develop a cluster-based protocol. This research uses a multichannel hierarchical clustering method and adds odd-even by dividing the network into several channels and forming a cluster head for each channel. The results of this research are Channel 1 with a throughput value of 1.88, channel 2 with a throughput value of 21.68, channel 3 with a throughput value of 1.62, and channel 4 with a throughput value of 42.44. The conclusion of this study is that the throughput results are smaller compared to the Multi-Channel Clustering H ierarchy method, because not all nodes are active
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metaheuristic algorithm,†Engineering Applications of Artificial Intelligence, vol. 133, no. 7, pp. 1081–1091, 2024, https:
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1114–1121, 2024, https://doi.org/10.1016/j.asoc.2024.111456.
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with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks,†Ad Hoc Networks, vol.
157, no. 4, pp. 1033–1043, 2024, https://doi.org/10.1016/j.adhoc.2024.103458.
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wireless powered sensor networks using tdma and noma,†Ad Hoc Networks, vol. 157, pp. 1034–1044, 4 2024, https://doi.org/
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[9] Y. Song, S. Zhang, and S. Wang, “An energy efficient fusing data gathering protocol in wireless sensor networks,†Computer
Networks, vol. 243, no. 4, pp. 1103–1113, 2024, https://doi.org/10.1016/j.comnet.2024.110305.
[10] P. Tripathy and P. Khilar, “Pso based amorphous algorithm to reduce localization error in wireless sensor network,†Pervasive
and Mobile Computing, vol. 100, no. 5, p. 10181028, 2024, https://doi.org/10.1016/j.pmcj.2024.101890.
[11] K. R. S. Kumar and S. Gopikrishnan, “Caddisfalcon optimization algorithm for on-demand energy transfer in wireless rechargeable
sensors based iot networks,†Sustainable Energy Technologies and Assessments, vol. 64, no. 4, pp. 1037–1047, 2024,
https://doi.org/10.1016/j.seta.2024.103732.
[12] A. Hag, D. Handayani, T. Pillai, T. Mantoro, M. H. Kit, and F. Al-Shargie, “Eeg mental stress assessment using hybrid multidomain
feature sets of functional connectivity network and time-frequency features,†Sensors, vol. 21, no. 9, pp. 6300–63 010,
2021, https://doi.org/10.3390/s21186300.
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networks,†Scientific African, vol. 24, no. 6, pp. 1894–1904, 2024, https://doi.org/10.1016/j.sciaf.2024.e02159.
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efficiency with cnn-fl and ngo optimization,†Measurement: Sensors, vol. 32, no. 4, pp. 1010–1022, 2024, https://doi.org/10.
1016/j.measen.2024.101057.
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Journal of Sensor and Actuator Networks, vol. 11, no. 1, pp. 73–84, 2022, https://doi.org/10.3390/jsan11040073.
[16] S. S. Babu and N. Geethanjali, “Lifetime improvement of wireless sensor networks by employing trust index optimized cluster
head routing (tiochr),†Measurement: Sensors, vol. 32, no. 4, pp. 1010–1020, 2024, https://doi.org/10.1016/j.measen.2024.
101068.
[17] S. Jaiswal and M. S. Ballal, “Fuzzy inference based irrigation controller for agricultural demand side management,†Computers
and Electronics in Agriculture, vol. 175, no. 8, pp. 1055–1065, 2020, https://doi.org/10.1016/j.compag.2020.105537.
[18] J.-Y. Lee, B. Lim, and Y.-C. Ko, “Performance analysis of multi-hop low earth orbit satellite network over mixed rf/fso links,â€
ICT Express, vol. 110, no. 3, 2024, https://doi.org/10.1016/j.icte.2024.03.004.
[19] C. R. K. J, R. D. Kulkarni, and D. M. Majid, “Energy-efficient architecture for high-performance fir adaptive filter using
hybridizing csdtcse-crabra based distributed arithmetic design: Noise removal application in iot-based wsn,†Integration, vol. 97,
no. 7, pp. 167 – 260, 2024, https://doi.org/10.1016/j.vlsi.2024.102172.
[20] A. Asha, R. Arunachalam, I. Poonguzhali, S. Urooj, and S. Alelyani, “Optimized rnn-based performance prediction of iot and
wsn-oriented smart city application using improved honey badger algorithm,†Measurement, vol. 210, no. 3, pp. 241–263, 2023,
https://doi.org/10.1016/j.measurement.2023.112505.
[21] R. Duan, A. He, G. Wu, G. Yang, and J. Zhang, “A trustworthy data collection scheme based on active spot-checking in uavassisted
wsns,†Ad Hoc Networks, vol. 158, no. 5, pp. 1570 – 8705, 2024, https://doi.org/10.1016/j.adhoc.2024.103477.
[22] A. Jalili, M. Gheisari, J. A. Alzubi, C. Fernndez-Campusano, F. Kamalov, and S. Moussa, “A novel model for efficient cluster
head selection in mobile wsns using residual energy and neural networks,†Measurement: Sensors, vol. 33, no. 6, pp. 1570 –
8705, 2024, https://doi.org/10.1016/j.measen.2024.101144.
[23] K. Ryu and W. Kim, “Energy efficient deployment of aerial base stations for mobile users in multi-hop uav networks,†Ad Hoc
Networks, vol. 157, no. 4, pp. 167 – 260, 2024, https://doi.org/10.1016/j.adhoc.2024.103463.
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Published
2024-07-01
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[1]
R. Rizky, Z. Hakim, S. Setiyowati, S. susilawati, and A. M. Yunita, “Development of the Multi-Channel Clustering Hierarchy Method for Increasing Performance in Wireless Sensor Network”, MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 3, pp. 603–614, Jul. 2024, doi: 10.30812/matrik.v23i3.3348.
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