Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks

2020 ◽  
Vol 154 ◽  
pp. 481-490 ◽  
Author(s):  
S. Vimal ◽  
Manju Khari ◽  
Rubén González Crespo ◽  
L. Kalaivani ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Ant Colony Optimization ◽  
Cognitive Radio Networks ◽  
Learning Algorithm ◽  
Radio Networks ◽  
Ant Colony ◽  
Q Learning ◽  
Multiobjective Ant Colony Optimization

scholarly journals Bio-inspired route estimation in cognitive radio networks

2020 ◽  
Vol 10 (3) ◽  
pp. 3095
Author(s):  
Miguel Tuberquia ◽  
Hans Lopez-Chavez ◽  
Cesar Hernandez
Keyword(s):  
Cognitive Radio ◽  
Ant Colony Optimization ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Ant Colony ◽  
Secondary Users ◽  
Mobility Spectrum ◽  
Hybrid Strategy ◽  
Primary Users ◽  
Specific Radio

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.

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