Genetic algorithms in wireless networking: techniques, applications, and issues

Mehboob, Usama; Qadir, Junaid; Salman, A.J.; Vasilakos, Athanasios V.

doi:10.1007/s00500-016-2070-9

Cited by 107 publications

(70 citation statements)

References 189 publications

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“…In 2016, the work proposed in [20] provided a compressive description of genetic algorithms (GAs) and a large number of techniques implemented mainly focused on wireless communications networks. The paper presents an explanation of GAs, their terminology, definitions, types, components, operators, coding and fitness functions to introduce the readers into the scope and limitations of GAs.…”

Section: New Approaches In Cognitive Radios Using Evolutionary Algorimentioning

confidence: 99%

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New Approaches in Cognitive Radios using Evolutionary Algorithms

Tuberquia¹,

Hernández²

2018

IJECE

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Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined.

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Section: New Approaches In Cognitive Radios Using Evolutionary Algorimentioning

confidence: 99%

“…The purpose is to preserve chromosomes and give the chance of reproducing chromosomes with better solutions. Besides, the operators mentioned above, the genetic algorithms include other essential components such as initial population, genetic representation, fitness function, and mechanism for selection [20].…”

Section: Genetics Algorithms (Ga)mentioning

confidence: 99%

New Approaches in Cognitive Radios using Evolutionary Algorithms

Tuberquia¹,

Hernández²

2018

IJECE

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“…GA can be utilized to find optimized solutions to examine problems through the application of biologically inspired methods [38][39][40]. Holland referred to the chromosomes as strings of binary symbols encoding a candidate solution to the given problem.…”

Section: Introductionmentioning

confidence: 99%

“…Holland referred to the chromosomes as strings of binary symbols encoding a candidate solution to the given problem. Wireless networks make use of the GA due to its well-known and remarkable generality and versatility and have been applied in a wide variety of settings in wireless communication networks [40]. The work in [41,42] focuses on the optimization of probability of detection and false alarm in CRN to minimize probability of error of a particular SU in a centralized CRN with GA.…”

Section: Introductionmentioning

confidence: 99%

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

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In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

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“…This dynamic, and often unknown, operating conditions increases wireless networking standards reliance on machine learning and artificial intelligence algorithms (Mehboob et al, 2014). Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm-based Framework for Soft Handoff Optimization in Wireless Networks

Asuquo¹,

Robinson²

2017

SET

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In this paper, a genetic algorithm (GA)-based approach is used to evaluate the probability of successful handoff in heterogeneous wireless networks (HWNs) so as to increase capacity and network performance. The traditional handoff schemes are prone to ping pong and corner effects and developing an optimized handoff scheme for seamless, faster, and less power consuming handoff decision is challenging. The GA scheme can effectively optimize soft handoff decision by selecting the best fit network for the mobile terminal (MT) considering quality of service (QoS) requirements, network parameters and user's preference in terms of cost of different attachment points for the MT. The robustness and ability to determine global optima for any function using crossover and mutation operations makes GA a promising solution. The developed optimization framework was simulated in Matrix Laboratory (MATLAB) software using MATLAB's optima tool and results show that an optimal MT attachment point is the one with the highest handoff success probability ( )value which determines direction for successful handoff in HWN environment. The system maintained a 90% with 4 channels and more while a 75% was obtained even at high traffic intensity.

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Genetic algorithms in wireless networking: techniques, applications, and issues

Cited by 107 publications

References 189 publications

New Approaches in Cognitive Radios using Evolutionary Algorithms

New Approaches in Cognitive Radios using Evolutionary Algorithms

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

A Genetic Algorithm-based Framework for Soft Handoff Optimization in Wireless Networks

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