Optimasi Konsumsi Energi pada Wireless Sensor Network (WSN) Menggunakan Algoritma Pengklasteran untuk Pertanian Presisi
Kata Kunci:
Energy-Efficient Clustering Protocol, Precision Agriculture IoT-WSN, Metaheuristic Optimization Algorithm, Network Lifetime Prolongation, Optimal Cluster Head Selection, Sustainable Smart Farming InfrastructureAbstrak
Keterbatasan energi pada node sensor menjadi tantangan kritis dalam menerapkan Jaringan Sensor Nirkabel (WSN) untuk pertanian presisi, di mana pergantian baterai di lahan yang luas seringkali tidak praktis baik dari sudut pandang teknis maupun ekonomi. Penelitian ini bertujuan untuk mengoptimalkan konsumsi energi melalui pengembangan algoritma pengelompokan hibrida yang dirancang khusus untuk mengurangi ketidakseimbangan beban kerja di antara node. Metodologi yang diusulkan, Optimized Clustering Agriculture (OCA), menggunakan fungsi kebugaran multi-tujuan yang menggabungkan energi sisa, jarak Euclidean ke Stasiun Basis, dan kepadatan tetangga untuk memilih Kepala Kluster (CH) yang optimal.
Pemodelan ini dilakukan dengan menggunakan model radio orde pertama untuk mengukur disipasi energi selama fase transmisi dan agregasi data. Hasilnya menunjukkan bahwa algoritma OCA secara signifikan lebih unggul daripada protokol konvensional seperti LEACH, dengan peningkatan umur jaringan hingga 30% dan penurunan kegagalan node pertama (First Node Die) hingga putaran ke-2.450. Selain itu, sistem ini mempertahankan Rasio Pengiriman Paket (PDR) di atas 96%, memastikan keandalan data sensor untuk mengambil keputusan dalam pertanian cerdas. Hasil temuan ini memberikan kontribusi teoretis terhadap protokol jaringan yang hemat energi serta kontribusi praktis dalam menunjang infrastruktur Pertanian Cerdas yang berkelanjutan dan mandiri energi.
Unduhan
Referensi
[1] R. Kapoor and S. Sharma, “Glowworm Swarm Optimization (GSO) based energy efficient clustered target coverage routing in Wireless
Sensor Networks (WSNs),” Int. J. Syst. Assur. Eng. Manag., vol. 14, pp. 622–634, 2023, doi: 10.1007/s13198-021-01398-z.
[2] F. P. E. Putra, M. Dafid, and I. Syafi’i, “Firewall Implementation as a Computer Network Security Strategy for Data Protection,” Brill.
Res. Artif. Intell., vol. 5, no. 1, pp. 291–297, 2025, doi: 10.47709/brilliance.v5i1.6162.
[3] S. S. Vellela and R. Balamanigandan, “Optimized clustering routing framework to maintain the optimal energy status in the wsn mobile
cloud environment,” Multimed. Tools Appl., vol. 83, no. 3, pp. 7919–7938, 2024, doi: 10.1007/s11042-023-15926-5.
[4] S. K. Barnwal, A. Prakash, and D. K. Yadav, “Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless
Sensor Networks using Metaheuristic Routing Technique,” Wirel. Pers. Commun., vol. 130, no. 3, pp. 1575–1596, 2023, doi:
10.1007/s11277-023-10345-z.
[5] S. D. Mishra and D. Verma, “Energy-Efficient and Reliable Clustering with Optimized Scheduling and Routing for Wireless Sensor
Networks,” Multimed. Tools Appl., vol. 83, no. 26, pp. 68107–68133, 2024, doi: 10.1007/s11042-024-18623-z.
[6] H. K. Shakya et al., “Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and MultiHop Routing Optimization,” SN Comput. Sci., vol. 6, no. 1, 2025, doi: 10.1007/s42979-024-03540-7.
[7] H. Huangshui, F. Xinji, W. Chuhang, L. Ke, and G. Yuxin, “A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol
for Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 133, no. 4, pp. 2175–2202, 2023, doi: 10.1007/s11277-024-10860-7.
[8] N. Nathiya, C. Rajan, and K. Geetha, “A hybrid optimization and machine learning based energy-efficient clustering algorithm with selfdiagnosis data fault detection and prediction for WSN-IoT application,” Peer-to-Peer Netw. Appl., vol. 18, no. 2, 2025, doi:
10.1007/s12083-024-01892-8.
[9] S. He, Q. Li, M. Khishe, A. Salih Mohammed, H. Mohammadi, and M. Mohammadi, “The optimization of nodes clustering and multihop routing protocol using hierarchical chimp optimization for sustainable energy efficient underwater wireless sensor networks,” Wirel.
Networks, vol. 30, no. 1, pp. 233–252, 2024, doi: 10.1007/s11276-023-03464-9.
[10] A. Srivastava and R. Paulus, “ELR-C: A Multi-objective Optimization for Joint Energy and Lifetime Aware Cluster Based Routing for
WSN Assisted IoT,” Wirel. Pers. Commun., vol. 132, no. 2, pp. 979–1006, 2023, doi: 10.1007/s11277-023-10645-4.
[11] R. Ramya and K. Padmapriya, “An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle
Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA),” J. Intell. Fuzzy Syst., vol. 44, no. 1, pp. 595–610, 2023, doi:
10.3233/JIFS-220963.
[12] V. K. H. Prasad and S. Periyasamy, “Energy Optimization-Based Clustering Protocols in Wireless Sensor Networks and Internet of
Things-Survey,” Int. J. Distrib. Sens. Networks, vol. 2023, 2023, doi: 10.1155/2023/1362417.
[13] V. Prakash, D. Singh, S. Pandey, S. Singh, and P. K. Singh, “Energy-Optimization Route and Cluster Head Selection Using M-PSO and
GA in Wireless Sensor Networks,” Wirel. Pers. Commun., 2024, doi: 10.1007/s11277-024-11096-1.
[14] B. Lonkar, A. Kuthe, P. Charde, A. Dehankar, and R. Kolte, “Optimal hybrid energy-saving cluster head selection for wireless sensor
networks: an empirical study,” Peer-to-Peer Netw. Appl., vol. 18, no. 4, 2025, doi: 10.1007/s12083-025-02002-y.
[15] T. Sharma, A. Balyan, and A. K. Singh, “Machine Learning-Based Energy Optimization and Anomaly Detection for Heterogeneous
Wireless Sensor Network,” SN Comput. Sci., vol. 5, no. 6, 2024, doi: 10.1007/s42979-024-03113-8.
[16] E. Silambarasan, E. Naresh, V. Asha, and M. R. Lamani, “Energy efficient heterogeneous clustering scheme using improved golden eagle
optimization algorithm for WSN-based IoT,” Int. J. Inf. Technol., vol. 17, no. 3, pp. 1753–1760, 2025, doi: 10.1007/s41870-024-02029-
z.
[17] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On
Websites Using Owasp Zap,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 348–355, 2024, doi: 10.47709/brilliance.v4i1.4227.
[18] F. P. E. Putra, U. Ubaidi, D. Mayangsari, and N. Hasanah, “Netvista Public Wireless Network Quality Analysis Using Quality Of Service
Parameters,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 443–452, 2024, doi: 10.47709/brilliance.v4i1.4388.
[19] F. P. E. Putra, U. Ubaidi, R. O. F. Kusuma, A. M. Syam, and S. A. Efendy, “Effect Of Distance On Wi-Fi Signal Quality In The Home
Environment,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 391–398, 2024, doi: 10.47709/brilliance.v4i1.4319.
[20] A. Javadpour, A. K. Sangaiah, H. Zaviyeh, and F. Ja’fari, “Enhancing Energy Efficiency in IoT Networks Through Fuzzy Clustering and
Optimization,” Mob. Networks Appl., vol. 29, no. 5, pp. 1594–1617, 2024, doi: 10.1007/s11036-023-02273-w.
[21] N. Malisetti and V. K. Pamula, “Energy aware cluster-based routing in WSN using hybrid pelican-blue monkey optimization algorithm,”
Evol. Intell., vol. 17, no. 4, pp. 2555–2575, 2024, doi: 10.1007/s12065-023-00903-6.
[22] A. A. Elsway, A. M. Khedr, O. Alfawaz, and W. Osamy, “Energy-aware disjoint dominating sets-based whale optimization algorithm
for data collection in WSNs,” J. Supercomput., vol. 79, no. 4, pp. 4318–4350, 2023, doi: 10.1007/s11227-022-04814-8.
[23] Y. Zhang, L. Yang, and Y. Tan, “Energy-efficient adaptive routing in heterogeneous wireless sensor networks via hybrid PSO and
dynamic clustering,” 2025, Springer. doi: 10.1186/s13677-025-00768-3.
[24] W. Osamy, A. M. Khedr, A. A. Elsawy, P. V. Pravija Raj, and A. Aziz, “SEACDSC: secure and energy-aware clustering based on discrete
sand cat swarm optimization for IoT-enabled WSN applications,” Wirel. Networks, vol. 30, no. 4, pp. 2781–2800, 2024, doi:
10.1007/s11276-024-03682-9.
[25] V. Verma and V. K. Jha, “Secure and Energy-Aware Data Transmission for IoT-WSNs with the Help of Cluster-Based Secure Optimal
Routing,” Wirel. Pers. Commun., vol. 134, no. 3, pp. 1665–1686, 2024, doi: 10.1007/s11277-024-10983-x.
[26] Rekha and R. Garg, “K-LionER: meta-heuristic approach for energy efficient cluster based routing for WSN-assisted IoT networks,”
Cluster Comput., vol. 27, no. 4, pp. 4207–4221, 2024, doi: 10.1007/s10586-024-04280-2.
[27] J. Paruvathavardhini and B. Sargunam, “Stochastic Bat Optimization Model for Secured WSN with Energy-Aware Quantized Indexive
Clustering,” J. Sensors, vol. 2023, 2023, doi: 10.1155/2023/4237198.
[28] F. Mir and F. Meziane, “Unequal-radius clustering in WSN-based IoT networks: energy optimization and load balancing in UDCOPA
protocol,” J. Supercomput., vol. 80, no. 19, pp. 26890–26921, 2024, doi: 10.1007/s11227-024-06426-w.
[29] N. Nathiya, C. Rajan, and K. Geetha, “An energy-efficient cluster routing for internet of things-enabled wireless sensor network using
mapdiminution-based training-discovering optimization algorithm,” Sadhana - Acad. Proc. Eng. Sci., vol. 49, no. 1, 2024, doi:
10.1007/s12046-023-02371-1.
[30] M. R. Senkumar, I. S. Arafat, R. Nathiya, and S. M. H. Nishath, “Enhanced Energy Efficient Clustering and Routing Algorithm in
Wireless Sensor Network,” Wirel. Pers. Commun., vol. 138, no. 3, pp. 1531–1558, 2024, doi: 10.1007/s11277-024-11549-7.
[31] C. Lei, “An energy-aware cluster-based routing in the Internet of things using particle swarm optimization algorithm and fuzzy
clustering,” 2024, Springer. doi: 10.1186/s44147-024-00464-0.
[32] S. Bharany, S. Sharma, N. Alsharabi, E. Tag Eldin, and N. A. Ghamry, “Energy-efficient clustering protocol for underwater wireless
sensor networks using optimized glowworm swarm optimization,” 2023, frontiersin.org. doi: 10.3389/fmars.2023.1117787.
[33] F. P. E. Putra, M. Surur, M. Mahendra, and G. Arifin, “Internet Network QOS Analysis at Yala Kopitiam pamekasan Using Wireshak,”
Brill. Res. Artif. Intell., vol. 5, no. 1, pp. 190–200, 2025, doi: 10.47709/brilliance.v5i1.5940.
[34] S. K. Sharma and M. Chawla, “PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network
Application in Internet of Things,” Wirel. Pers. Commun., vol. 133, no. 2, pp. 1243–1263, 2023, doi: 10.1007/s11277-023-10814-5.
[35] E. Heidari, “A novel energy-aware method for clustering and routing in IoT based on whale optimization algorithm & Harris Hawks
optimization,” Computing, vol. 106, no. 3, pp. 1013–1045, 2024, doi: 10.1007/s00607-023-01252-z.
[36] F. P. E. Putra, R. M. Ilhamsyah, S. A. Efendy, and A. Rizki, “Implementation And Evaluation Of Zerotier-Based Virtual Network For
Device Connectivity,” Brill. Res. Artif. Intell., vol. 5, no. 1, pp. 281–290, 2025, doi: 10.47709/brilliance.v5i1.5966.
[37] A. Saeedi, M. Kuchaki Rafsanjani, and S. Yazdani, “Energy efficient clustering in IoT-based wireless sensor networks using binary whale
optimization algorithm and fuzzy inference system,” J. Supercomput., vol. 81, no. 1, 2025, doi: 10.1007/s11227-024-06556-1.
[38] A. Choudhary and N. C. Barwar, “Optimizing Clustering in Wireless Sensor Networks: A Synergistic Approach Using Reinforcement
Learning (RL) and Particle Swarm Optimization (PSO),” SN Comput. Sci., vol. 5, no. 6, 2024, doi: 10.1007/s42979-024-03080-0.
[39] P. P. I. Vazhuthi, A. Prasanth, S. P. Manikandan, and K. K. D. Sowndarya, “A hybrid ANFIS reptile optimization algorithm for energyefficient inter-cluster routing in internet of things-enabled wireless sensor networks,” Peer-to-Peer Netw. Appl., vol. 16, no. 2, pp. 1049–
1068, 2023, doi: 10.1007/s12083-023-01458-0.
[40] K. S, D. D. Rao, A. Jain, S. Sharma, S. V. Pandit, and R. Pandey, “Effectual Energy Optimization Stratagems for Wireless Sensor Network
Collections Through Fuzzy-Based Inadequate Clustering,” SN Comput. Sci., vol. 5, no. 8, 2024, doi: 10.1007/s42979-024-03377-0.
[41] A. F. Rachman, F. P. E. Putra, S. Syirofi, and D. Wahid, “Case Study of Computer Network Development for the Internet Of Things
(IoT) Industry in an Urban Environment,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 399–407, 2024, doi: 10.47709/brilliance.v4i1.4302.
[42] F. P. E. Putra, U. Ubaidi, M. Aziz, M. Irfan, and R. Alim, “Improving Network Service Quality in parts of Sampang City: QoS Evaluation
and User Perception of QoE,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 408–412, 2024, doi: 10.47709/brilliance.v4i1.4311.
[43] Fauzan Prasetyo Eka Putra, Dea Aulia Siswoyo, M. Idris Ainul Yaqin, and Rica Oktavia, “Tinjauan Regulasi Siber dan Kebijakan
Keamanan Jaringan 5G: Perspektif Nasional dan Internasional,” 2025, researchgate.net. doi: 10.55606/jitek.v5i1.6141.
[44] F. P. E. Putra, M. Irfan, M. Aziz, and R. N. Saputra, “Wireless Network Design at Pamekasan Regency Public Library,” Brill. Res. Artif.
Intell., vol. 5, no. 1, pp. 144–150, 2025, doi: 10.47709/brilliance.v5i1.5876.
[45] P. Karpurasundharapondian and M. Selvi, “A comprehensive survey on optimization techniques for efficient cluster based routing in
WSN,” Peer-to-Peer Netw. Appl., vol. 17, no. 5, pp. 3080–3093, 2024, doi: 10.1007/s12083-024-01678-y.
[46] S. Tumula et al., “An opportunistic energy-efficient dynamic self-configuration clustering algorithm in WSN-based IoT networks,” Int.
J. Commun. Syst., vol. 37, no. 1, 2024, doi: 10.1002/dac.5633.
[47] M. Selvi, G. Kalaiarasi, S. C. Mana, R. Yogitha, and R. Padmavathy, “Energy and Security Aware Hybrid Optimal Cluster-based Routing
in Wireless Sensor Network,” Wirel. Pers. Commun., vol. 137, no. 3, pp. 1395–1422, 2024, doi: 10.1007/s11277-024-11288-9.
[48] P. Kaur, R. Garg, and V. Kukreja, “Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature
review,” Telecommun. Syst., vol. 84, no. 1, pp. 115–151, 2023, doi: 10.1007/s11235-023-01037-x.
[49] J. de Boniface, T. F. Tvedskov, and ..., “Omitting axillary dissection in breast cancer with sentinel-node metastases,” … Engl. J. …, 2024,
doi: 10.1056/NEJMoa2313487.
[50] R. Das and M. Dwivedi, “Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm
for HWSN,” J. Reliab. Intell. Environ., 2024, doi: 10.1007/s40860-022-00200-6.
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Mohammad Rizki Hoirur Rofi, Imam Ghozeli (Penulis)
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
