Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Eki Renaldi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

 1 
Optimasi Posisi Relay Node pada Wireless Sensor Network menggunakan Algoritma Genetika Eki Renaldi; Primantara Hari Trisnawan; Kasyful Amron
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Relay is one of the most important devices to forward sensory data to sink. Therefore, relay's connectivity must be kept well so Wireless Sensor Network works optimally. Common problem in Wireless Sensor Network is relay node placement in a deterministic way is not getting optimal results. Heuristic way is done with hope that optimal position of relay can be achieved. In this research 3 scenarios with static relay and internet gateway locations are carried out. Scenario 1's area is 90x77 and has 45 relays. Scenario 2's area is 60x77 and has 31 relays. Scenario 3's area is 120x77 and has 59 relays. In this research genetic algorithm is carried out to solve relay's position optimization. Genetic algorithm is an algorithm that can be used to find the nearly optimum solution from wide possibility solution area. In this research, researcher used extended intermediate crossover, random mutation, and elitism selection. The results from this research are early convergence happened at 752nd generation for scenario 1, 846th generation for scenario 2, and 424th generation for scenario 3. Population size for scenario 1 is 150 scenario 2's is 100 and scenario 3's is . Ratio for scenario 1 is 0,6 and for scenario 2 and scenario 3 are 0,7. The result from this research is that relays position optimization using genetic algorithm is not as good as using deterministic because fitness from genetic algorithm is not greater as fitness from deterministic and genetic algorithm needs longer time.
Co-Authors Kasyful Amron Primantara Hari Trisnawan