An Intelligent Approach for Resource Management in SDN-VANETs Using Fuzzy Logic

2019 ◽  
pp. 747-756 ◽  
Author(s):  
Ermioni Qafzezi ◽  
Kevin Bylykbashi ◽  
Evjola Spaho ◽  
Leonard Barolli
Keyword(s):  
Fuzzy Logic ◽  
Resource Management ◽  
Intelligent Approach

Fuzzy Logic Resource Management and Coevolutionary Game-based Optimization

2001 ◽  
Author(s):  
James F. Smith ◽  
Rhyne III ◽  
II Robert D.
Keyword(s):  
Fuzzy Logic ◽  
Resource Management ◽  
Logic Resource

scholarly journals A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques

2010 ◽  
Vol 2 (2) ◽  
pp. 273-284 ◽  
Author(s):  
I. K. Tabash ◽  
M. A. Mamun ◽  
A. Negi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Probability Function ◽  
Active Queue Management ◽  
Buffer Size ◽  
Queue Management ◽  
Passive Devices ◽  
Better Regulation ◽  
Ip Routers ◽  
Intelligent Approach

Conventional IP routers are passive devices that accept packets and perform the routing function on any input. Usually the tail-drop (TD) strategy is used where the input which exceeds the buffer capacity are simply dropped. In active queue management (AQM) methods routers manage their buffers by dropping packets selectively. We study one of the AQM methods called as random exponential marking (REM). We propose an intelligent approach to AQM based on fuzzy logic controller (FLC) to drop packets dynamically, keep the buffer size around desired level and also prevent buffer overflow. Our proposed approach is based on REM algorithm, which drops the packets by drop probability function. In our proposal we replace the drop probability function by a FLC to drop the packets, stabilize the buffer around the desired size and reduce delay. Simulation results show a better regulation of the buffer.  Keywords: Random exponential marking; Active queue management; Fuzzy logic controller; Pro-active queue management. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.2786               J. Sci. Res. 2 (2), 273-284 (2010) 

scholarly journals A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

2021 ◽  
Vol 10 (4) ◽  
pp. 37-42
Author(s):  
M.P.L. Perera
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Artificial Intelligent ◽  
Human Decision ◽  
Decision Making Processes ◽  
E Learning ◽  
Intelligent Approach ◽  
Learning Frameworks

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

Evaluating the Degree of Trust Under Context Sensitive Relational Database Hierarchy Using Hybrid Intelligent Approach

2015 ◽  
Vol 2 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Manash Sarkar ◽  
Soumya Banerjee ◽  
Aboul Ella Hassanien
Keyword(s):  
Fuzzy Logic ◽  
Relational Model ◽  
Adjacent Level ◽  
Trust Value ◽  
Agent Based ◽  
Real Application ◽  
Context Sensitive ◽  
Trust Relationships ◽  
Dynamic Attributes ◽  
Intelligent Approach

Trust is a binary value either 0 or 1which is not measurable in real application. An entity can either trust another entity completely or do not trust at all. Trust between different entities in generalization-specialization relational model in database varies with respect to adjacent level of hierarchy. Trust value among entities depends not only on hierarchy level but also the context in which entities belong. In this paper the authors propose a model that allows evaluating trust relationships between two entities in the same context. Basically the trust depends on parameters like knowledge, experience and recommendation. In real application it is not always possible to have prior knowledge about mentioned entities. Extending this theory, it is shown that how the trust relationships is evaluated between different entities in different contexts of specialization level. As the trust represents uncertainty therefore, fuzzy logic is used to overcome the concept of binary value for trust. Considering the dynamic attributes of an entity based system, an appropriate agent based methodology could be incorporated. This paper introduced a model called FACO based on Fuzzy logic and Ant colony to calculate both the trust and the updated trust value among entities.

A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

Measurement ◽  
2015 ◽  
Vol 66 ◽  
pp. 26-34 ◽  
Author(s):  
Hemad Zareiforoush ◽  
Saeid Minaei ◽  
Mohammad Reza Alizadeh ◽  
Ahmad Banakar
Keyword(s):  
Computer Vision ◽  
Fuzzy Logic ◽  
Quality Measurement ◽  
Milled Rice ◽  
Intelligent Approach

Interval type-2 fuzzy logic based radio resource management in multi-radio WSNs

2018 ◽  
Vol 35 (2) ◽  
pp. 2525-2536 ◽  
Author(s):  
Wei Peng ◽  
Dongyan Chen ◽  
Wenhui Sun ◽  
Chengdong Li ◽  
Guiqing Zhang
Keyword(s):  
Fuzzy Logic ◽  
Resource Management ◽  
Radio Resource Management ◽  
Radio Resource ◽  
Interval Type
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