Optimasi Penempatan Node pada WSN dengan Adaptive PSO untuk Efisiensi Energi dan Cakupan Optimal
Kata Kunci:
Wireless Sensor Network, Adaptive Particle Swarm Optimization, Efisiensi Energi, Cakupan Area, Optimasi NodeAbstrak
Perkembangan teknologi Wireless Sensor Network (WSN) telah mendorong penerapan luas dalam sistem pemantauan lingkungan dan Internet of Things (IoT). Namun, keterbatasan energi node sensor dan ketidakseimbangan distribusi penempatan node masih menjadi tantangan utama yang berdampak pada efisiensi energi dan area blankspot. Penelitian ini bertujuan mengoptimalkan penempatan node sensor pada WSN agar mencapai efisiensi energi maksimum dan cakupan area optimal melalui penerapan algoritma Adaptive Particle Swarm Optimization (APSO). Penelitian ini menggunakan pendekatan studi kuantitatif berbasis simulasi eksperimental, dengan model jaringan dua dimensi berukuran 100×100 meter dan jumlah node sebanyak 50 unit. Parameter APSO, seperti inertia weight dan koefisien percepatan, disesuaikan secara adaptif untuk menyeimbangkan eksplorasi dan eksploitasi. Evaluasi dilakukan dengan membandingkan performa APSO terhadap PSO klasik dan metode penempatan acak menggunakan indikator rasio cakupan area, konsumsi energi total, serta waktu konvergensi. Hasil simulasi menunjukkan bahwa APSO mampu meningkatkan cakupan area hingga 96,8%, menghemat energi sebesar 31,5%, dan mempercepat konvergensi hingga 23% dibandingkan PSO klasik. Distribusi node yang dihasilkan lebih merata, mengurangi area kosong, serta memperpanjang masa hidup jaringan. Penerapan APSO terbukti efektif dalam mengatasi keterbatasan energi dan optimasi cakupan pada WSN. Tujuan penelitian tercapai dengan peningkatan signifikan pada efisiensi dan kinerja jaringan. Studi lanjutan disarankan untuk mengembangkan penerapan APSO pada optimasi routing dinamis dan manajemen energi adaptif di sistem smart environment dan IoT berskala besar.
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