Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

EVALUATION OF FUZZY RULES EXTRACTED FROM DATA

2001 ◽  
Vol 06 (02) ◽  
Author(s):  
Adam Schenker ◽  
Abraham Kandel ◽  
Mark Last
Keyword(s):  
Fuzzy Rules

Tuning of PI Controller for A Conical Tank Process with Fuzzy Rules

2014 ◽  
Vol 2 (3) ◽  
pp. 6-10
Author(s):  
Gireesh Namineni ◽  
◽  
G. Sreenivasulu ◽  
Keyword(s):  
Pi Controller ◽  
Fuzzy Rules

Anomaly intrusion detection based on genetic optimization and fuzzy rules mining

2009 ◽  
Vol 29 (8) ◽  
pp. 2227-2229
Author(s):  
Dong-sheng XU ◽  
Xiao-yan AI ◽  
Shi-liang YAN
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Rules ◽  
Genetic Optimization ◽  
Anomaly Intrusion Detection

Images classification based on combination of positive and negative fuzzy rules

2011 ◽  
Vol 31 (1) ◽  
pp. 243-246
Author(s):  
Jun WU ◽  
Shi-tong WANG
Keyword(s):  
Fuzzy Rules

On ε-insensitive simplification of fuzzy rules for fetal distress assessment

2021 ◽  
pp. 115052
Author(s):  
Michal Jezewski ◽  
Robert Czabanski ◽  
Jacek M. Leski ◽  
Adam Matonia ◽  
Radana Kahankova
Keyword(s):  
Fetal Distress ◽  
Fuzzy Rules
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