scholarly journals CDLN - CLUSTER DISTANCE BASED DATA FORWARDING AND OPTIMAL LEADER ELECTION USING FUZZY INFERENCE IN WIRELESS NETWORK

2021 ◽  
Vol 12 (4) ◽  
pp. 1074-1083
Author(s):  
Sachidanand S Joshi ◽  
Sangappa Ramachandra Biradar
Keyword(s):  
Wireless Network ◽  
Fuzzy Inference ◽  
Leader Election ◽  
Data Forwarding ◽  
Cluster Distance

scholarly journals Motion Adaptive Vertical Handoff in Cellular/WLAN Heterogeneous Wireless Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Limin Li ◽  
Lin Ma ◽  
Yubin Xu ◽  
Yunhai Fu
Keyword(s):  
Wireless Network ◽  
Cellular Network ◽  
Fuzzy Inference ◽  
Vertical Handoff ◽  
Heterogeneous Wireless Network ◽  
Inference System ◽  
Q Learning ◽  
Adaptive Ability ◽  
Unnecessary Handoff ◽  
Motion Types

In heterogeneous wireless network, vertical handoff plays an important role for guaranteeing quality of service and overall performance of network. Conventional vertical handoff trigger schemes are mostly developed from horizontal handoff in homogeneous cellular network. Basically, they can be summarized as hysteresis-based and dwelling-timer-based algorithms, which are reliable on avoiding unnecessary handoff caused by the terminals dwelling at the edge of WLAN coverage. However, the coverage of WLAN is much smaller compared with cellular network, while the motion types of terminals can be various in a typical outdoor scenario. As a result, traditional algorithms are less effective in avoiding unnecessary handoff triggered by vehicle-borne terminals with various speeds. Besides that, hysteresis and dwelling-timer thresholds usually need to be modified to satisfy different channel environments. For solving this problem, a vertical handoff algorithm based on Q-learning is proposed in this paper. Q-learning can provide the decider with self-adaptive ability for handling the terminals’ handoff requests with different motion types and channel conditions. Meanwhile, Neural Fuzzy Inference System (NFIS) is embedded to retain a continuous perception of the state space. Simulation results verify that the proposed algorithm can achieve lower unnecessary handoff probability compared with the other two conventional algorithms.

scholarly journals Energy Consumption Between Routing and Bridging Protocol for Eco-Friendly Wireless Networks

2019 ◽  
pp. 223-232
Author(s):  
Rinanza Zulmy Alhamri ◽  
Toga Aldila Cinderatama ◽  
Agustono Heriadi ◽  
Andika Kurnia Adi Pradana
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Wireless Network ◽  
Video Streaming ◽  
Routing Protocol ◽  
Data Access ◽  
Packet Size ◽  
Data Forwarding ◽  
Cpu Usage ◽  
Network Device

The implementation of eco-friendly technology has been become an interesting field for sustainability. No exception with the implementation of wireless technology that used for developing networks infrastructure, it is necessary for saving the usage of energy. As a data forwarding protocol in a computer network, commonly there are two protocols that used for, which are routing and bridging protocol. Technically routing protocol has been confirmed that it is more effective and efficient than bridging protocol. However bridging protocol still becomes the popular protocol for data forwarding because it is easy to use. This research tried to test the energy consumption of the wireless network device that implementing between routing or bridging protocol. The wireless network device that used for this research was MikroTik router RB 433Ah. The data forwarding protocol that was tested consists of bridging, static routing, and RIP routing. Data traffic scenario that used for this research consisted of two scenarios which were HTML data access with packet size 256B and video streaming data access with packet size 1518B. Measuring the energy consumption referred to three parameters which were power consumption, CPU usage, and processor temperature. The result showed that for HTML data access scenario, the RIP routing protocol become the lowest energy consumption with power consumption reached 7.460 W, CPU usage 4.6 %, and processor temperature 38.133^C. While for video streaming scenario, generally the RIP routing protocol still become the lowest energy consumption with power consumption reached 7.567 W, CPU usage 7.33 %, and processor temperature 36.727^C.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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