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

scholarly journals Smart Radio Resource Management for Content Delivery Services in 5G and Beyond Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Dominik Neznik ◽  
Lubomir Dobos ◽  
Jan Papaj
Keyword(s):  
Game Theory ◽  
Fuzzy Logic ◽  
Resource Management ◽  
Radio Resource Management ◽  
Content Delivery ◽  
Spectrum Allocation ◽  
Radio Resource ◽  
Advantages And Disadvantages ◽  
Delivery Services

In 5G networks, the spectrum allocation techniques play a very important part of the quality of content delivery services. The processes of channelling and device selection are important in the 5G technology and beyond with many access devices in networks to improve the quality of services. In this paper, we propose a method based on Fuzzy Logic, Game Theory, and Smart Method (which is a combination of Fuzzy Logic and Game Theory). These methods are suitable to improve the speed and quality of links of data routing in networks. The paper shows that effective spectrum allocation to devices is not an option but a requirement in a huge data flow environment of the wireless communications, if one wants to ensure acceptable speed and quality of the connection and to provide adequate quality of the services. Each of the selected methods for radio resource management has some advantages and disadvantages in the evaluation of results. The paper describes the process of channel allocation with different methods for IEEE 802.11xx networks that are in the focus of our research in the sphere of wireless communication. Companies use cloud computing to provide services and to share information, but there needs to be some radio resource management to effectively use the services in the wireless mobile environment because the number of different types of devices being connected to the wireless networks to create smart homes and smart cities is growing.

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Smart spectrum and radio resource management for future 5G networks

2021 ◽  
Vol 193 ◽  
pp. 108089
Author(s):  
Miguel López-Benítez ◽  
Alessandro Raschellà ◽  
Sara Pizzi ◽  
Li Wang ◽  
Marco Di Felice ◽  
...  
Keyword(s):  
Resource Management ◽  
Radio Resource Management ◽  
5G Networks ◽  
Radio Resource

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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