Adaptif Poly Frame PRMA pada Jaringan M2M Kognitif Kapiler

Penulis

  • Eko Arifianto Universitas Diponegoro
  • Aghus Sofwan Universitas Diponegoro
  • Teguh Prakoso Universitas Diponegoro

DOI:

https://doi.org/10.25126/jtiik.2020752281

Abstrak

Komunikasi mesin ke mesin (M2M) pada jaringan kapiler, menggunakan metode transmisi Packet Reservation Multiple Access (PRMA), dan struktur frame data frame biasa, serta skenario komunikasi event driven. Seiring dengan pertambahan perangkat, metode, struktur frame dan skenario komunikasi tersebut tidak dapat menangani laju data yang sangat banyak, sehingga terjadi kemacetan yang memperlambat komunikasi. Penelitian ini bertujuan membuat komunikasi M2M yang lancar walaupun perangkat bertambah banyak, dengan membuat struktur frame baru dan skenario komunikasi baru, berupa Adaptive Poly Frame (APF) serta Scheduler Update (SU). APF dan SU dirancang dengan memberikan nomor urut serta prioritas pada data, yang kemudian dioptimasi dengan meningkatkan peluang persaingan MK (O), jumlah siklus huni slot (B), jumlah siklus huni kanal (S), dan Transmisi Sukses PRMA (TSPRMA). Penelitian ini menghasilkan transmisi sukses 92-28%, optimasi transmisi sukses 93-30%, siklus transmisi 1,5-8,1% dan reduksi siklus transmisi 0,9-7,2%.

Abstract

Machine to machine (M2M) communication in capillary networks, using the Packet Reservation Multiple Access (PRMA) transmission method, and ordinary frame data frame structures, as well as event driven communication scenarios. Along with the addition of devices, methods, frame structures and communication scenarios cannot handle very large data rates, resulting congestion that results in inefficient communication. This research aims to make M2M communication efficient even though the device is multiplying, by creating new frame structures and new communication scenarios, in the form of Adaptive Poly Frame (APF) and Scheduler Update (SU). APF and SU are designed by sequence number and prioritizing data, which is then optimized by increase the chance of MK contestation (O), the number of slot occupancy cycles (B), the number of canal occupancy cycles (S) and PRMA Success Transmission (TSPRMA). This research resulted in 92-28% successful transmission, 93-30% successful transmission optimization, 1.5-8.1% transmission cycle and 0.9-7.2% transmission cycle reduction

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Referensi

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Diterbitkan

08-10-2020

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Adaptif Poly Frame PRMA pada Jaringan M2M Kognitif Kapiler. (2020). Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(5), 923-932. https://doi.org/10.25126/jtiik.2020752281