A New MIMO ANFIS-PSO Based NARMA-L2 Controller for nonlinear dynamic systems

The corresponding author is grateful to the Iraqi Ministry of Higher Education and Scientific Research for supporting the current research

Green Network Costs of 5G and Beyond, Expectations vs Reality

—Generally, it has been taken for granted that green technology provides clean and cheap energy, but often without consideration of the costs. In fact, there are many trade offs concurrent with enabling such technology. Accordingly, this paper evaluates and compares the green energy oriented mobile networkswiththeirtraditionalcounterparts.Itpresentsamathematical model that helps in understanding the different variables which are necessary to advocate the green/renewable method over the traditional form or vice versa. This research shows that the cost efficiency (CE) of green networks can be relatively high, about twice that of the traditional, which is represented by cloud radio access network. Based on experimental data, this research shows that green technology requires more operational control than the traditional form to produce the same amount of power.Withvariantsites,cities,countries,geographicalareasand equipment manufacturing characteristics, the proposed model can predict the futuristic total green network’s trade-offs. By doing so, the service providers, investors or network vendors will be able to decide upon an appropriate balance between both types of networks

End-to-End Delay Enhancement in 6LoWPAN Testbed Using Programmable Network Concepts

This paper introduces a proof-of-concept 6LoWPAN testbed to study the integration of programmable network technologies in relaxed throughput and low-power IoT devices. Open source software and hardware platforms are used in the implemented testbed to increase the possibility of future network extension. The proposed architecture offers end-to-end connectivity via the 6LoWPAN gateway to integrate IPv6 hosts and the low data rate devices directly. Nowadays, SoftwareDefined Networking (SDN) and Network Function Virtualization (NFV) are the most promising technologies for dealing with the massive increase in M2M devices and achieving agile traffic. The developed approach in this paper is entitled tailored Software Defined-Network Function Virtualization (SD-NFV), which is aimed at reducing the end-to-end delay and improving the energy depletion in sensor nodes. Experimental scenarios of the implemented testbed are conducted using a simple sensing application and the obtained results indicate that the introduced approach is appropriate for constrained IoT devices. By utilizing SD-NFV scheme in 6LoWPAN network, the data delivery ratio increased by 5-14%, the node operational time prolonged by 70%, the end-to-end latency for gathering sensor data minimized by ≈160%, and the latency for transmitting control messages to a specified node diminished by ≈63% when compared to a traditional (non SDN-enabled) 6LoWPAN network

Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment

© Copyright 2023 The Authors. Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges

A New Intelligent Approach for Optimising 6LoWPAN MAC Layer Parameters

Fairness, low latency, and high throughput with low energy consumption are desired attributes for Medium Access Control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical (PHY) layers standard for IPv6 over Low power Personal Area Network (6LoWPAN). When non-appropriate parameter setting is used, the default MAC parame-ters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies which investigate the impact of optimising these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to select the optimal 6LoWPAN MAC layer parameters set, the introduced mechanism depends on Artificial Neural Networks (ANN), Genetic Algorithm (GA) or Particles Swarm Optimisation (PSO) to select and validate the optimised MAC parameters. The obtained simulation results showed that utilising the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network

PSO-PD fuzzy control of distillation column

In this paper, PD-fuzzy logic control has been designed to control product compositions of distillation column. Manual and Particle Swarm Optimisation (PSO) procedures have been applied to tune scaling factors of the controller. Simulation is carried out to minimize the Integral of Square Error (ISE). System behaviour analysis shows very good performance and fast response

Evolutionary based optimisation of multivariable fuzzy control system of a binary distillation column

Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimisation (PSO) are population based stochastic search algorithms that categorised into the taxonomy of evolutionary optimisation. These methods have been employed independently to tune a fuzzy controller for maintaining the product compositions of a binary distillation column. An analytical investigation has been conducted to distinguish the optimal tuning approach of the controller among these techniques. Based on simulation results, particle swarm optimisation approach combined with the fuzzy controller is identified as a comparatively better configuration regarding its performance index and computational efficiency.The corresponding author is grateful to the Iraqi Ministry of Higher Education and Scientific Research for supporting the current research

Performance prediction of software defined network using an artificial neural network

An Artificial Neural Network has been proposed as predicting the performance of the Software Defined Network according to effective traffic parameters. Those used in this study are round-trip time, throughput and the flow table rules for each switch, POX controller and OpenFlow switches, which characterize the behaviour of the Software Defined Network, have been modelled and simulated via Mininet and Matlab platforms. An ANN has the ability to provide an excellent input-output relationship for nonlinear and complex processes. The network has been implemented using different topologies, one and two layers in the hidden zone with different numbers of neurons. Generalization of the prediction model has been tested with new data that are unseen in the training stage. The simulated results show reasonably good performance of the network.The Iraqi Ministry of Higher Education and Scientific Researc

A hybrid intelligent approach for optimising software-defined networks performance

© 2016 IEEE.A new hybrid intelligent approach for optimising the performance of Software-Defined Networks (SDN), based on heuristic optimisation methods integrated with neural network paradigm, is presented. Evolutionary Optimisation techniques, such as Particle Swarm Optimisation (PSO) and Genetic Algorithms (GA), are employed to find the best set of inputs that give the maximum performance of an SDN. The Neural Network model is trained and applied as an approximator of SDN behaviour. An analytical investigation has been conducted to distinguish the optimal optimisation approach based on SDN performance as an objective function as well as the computational time. After getting the general model of the Neural Network through testing it with unseen data, this model has been implemented with PSO and GA to find the best performance of SDN. The PSO approach combined with SDN, represented by ANN, is identified as a comparatively better configuration regarding its performance index as well as its computational efficiency.The corresponding author is grateful to the Iraqi Ministry of Higher Education and Scientific Research for supporting the current research

The use of artificial neural network to predict correlation of cementation factor to petrophysical properties in Yamamma formation

The cementation factor has specific effects on petrophysical properties in porous media. The accurate determination of this factor gives reliable saturation results and consequently hydrocarbon reserve calculations. Nasiriya oil field is the studied field, which is one of the giant oil fields in the south of Iraq. Five wells from NS-1 to NS-5 were studied wells. The study was made across Yamamma carbonate formation with depth interval from 3,156 m to 3,416 m. Environmental corrections had been made as per SLB charts 2005. Permeability, porosity, resistivity formation factor and cementation factor had been calculated using interactive petrophysical software. In this study, porosity, permeability and resistivity formation factor relationships to cementation factor were proposed using the artificial neural network model. This methodology provided very efficient performance and excellent prediction of cementation factor value with less than 10-4 mean square error (MSE). The results of this model showed that the cementation factor values ranged between 1.95 and 2.13