scholarly journals Genetic Algorithm Applied to Planning IEEE 802.11g Networks

Author(s):  
Hamid Barkouk ◽  
El Mokhtar En-Naimi ◽  
Aziz Mahboub

The problem of planning local wireless network IEEE 802.11g consists of automatically positioning and setting up wireless access points (APs) in order to provide access to the local network with the desired coverage and the required quality of service (QOS).In addition to the complexity of predicting the Quality of Service (QoS) of a network from the variables of the problem (positions, parameters and frequency of the APs), the planning of WLAN networks faces several difficulties. In particular, the location of APs and the allocation of frequencies. There is no single model to solve the problem of designing wireless local networks. Depending on the situations and the hypotheses studied, different criteria can be considered and expressed in terms of constraints to be observed or in terms of objectives to be optimized. The first distinction is to separate the financial criteria from the network quality criteria. The nature of these two criteria being fundamentally different. Then there are a variety of service quality criteria, but we can still group them into three main categories: coverage criteria, interference criteria and capacity criteria.. In this article, we will use an optimization method based on an algorithm of stochastic optimization, which is also based on the mechanisms of natural selection and of genetic. It is genetic algorithm. Our goal consist of minimizing the total interaction between the APs to perform the good choices when deploying a network 802.11g in a way that gives users signal-to-interference ratios (SIR) greater than the required threshold ß.

Author(s):  
D. A. Perepelkin ◽  
◽  
V. T. Nguyen ◽  

As a new network paradigm, software-defined networks (SDN) are able to cope with the limitations of traditional networks. SDNs use a management controller with a global view of the network and switching devices that act as packet forwarding equipment, known as «OpenFlow switches». Network resources limitations and ensuring quality of service requirements lead to an important need for load balancing between SDN switches. The purpose of work  research and analysis of load balancing processes in SDN based on genetic algorithm. To confirm the effectiveness and correctness of the genetic algorithm in SDN, the software for modeling the load balancing processes has been developed. The simulation results confirmed the efficiency of the genetic algorithm in SDN for balancing and redistributing network traffic in order to ensure the required quality of service and reduce network congestion.


2011 ◽  
Vol 16 (2) ◽  
pp. 133-152 ◽  
Author(s):  
James A. Brunetti ◽  
Kanti Chakrabarti ◽  
Alina M. Ionescu-Graff ◽  
Ramesh Nagarajan ◽  
Dong Sun

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.


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