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

Intelligent Control for distillation columns

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNowadays, industrial processes are having to be rapidly developed to meet high standards regarding increases in the production rate and/or improving product quality. Fulfilling these requirements is having to work in tandem with the pressure to reduce energy consumption due to global environmental regulations. Consequently, most industrial processes critically rely on automatic control, which can provide efficient solutions to meet such challenges and prerequisites. For this thesis, an intelligent system design has been investigated for controlling the distillation process, which is characterised by highly nonlinear and dynamic behaviour. These features raise very challenging tasks for control systems designers. Fuzzy logic and artificial neural networks (ANNs) are the main methods used in this study to design different controllers, namely: PI- PD- and PID-like fuzzy controllers, ANN-based NARMAL2 in addition to a conventional PID controller for comparison purposes. Genetic algorithm (GA) and particle swarm optimisation (PSO) have also been utilised to tune fuzzy controllers by finding the best set of scaling factors. Finally, an intelligent controller is proposed, called ANFIS-based NARMA-L2, which uses ANFIS as an approximation approach for identifying the underlying systems in a NARMA-L2 configuration. The controllers are applied to control two compositions of a binary distillation column, which has been modelled and simulated in MATLAB® and on the Simulink® platform. Comparative analysis has been undertaken to investigate the controllers’ performance, which shows that PID-like FLC outperforms the other tested fuzzy control configurations, i.e. PI- and PD-like. Moreover, PSO has been found to outperform GA in finding the best set of scaling factors and over a shorter time period. Subsequently, the performance of PID-like FLC has been compared with ANN-based NARMA-L2 and the proposed ANFIS-based NARMA-L2, by subjecting the controlled column to different test scenarios. Furthermore, the stability and robustness of the controllers have been assessed by subjecting the controlled column to inputs variance and disturbances situations. The proposed ANFIS-based NARMAL2 controller outperforms and demonstrates more tolerance of disturbances than the other controllers. Finally, the study has involved investigating the control of a multicomponent distillation column due to its significant enhancement in operational efficiency regarding energy saving and recent widespread implementation. That is, Kaibel’s distillation column with 4×4 configuration has been simulated also in MATLAB® and on the Simulink® platform with the proposed controller being implemented to control the temperatures of the column and the outcomes subsequently compared with conventional PID controllers. Again, the novel controller has proven its superiority regarding the disturbances tolerance as well as dealing with the high dynamics and nonlinear behaviour.Iraqi Ministry of Higher Education and Scientific Research (Iraqi Cultural Attaché in London

ANN-based prediction of cementation factor in carbonate reservoir

Since carbonate reservoirs are a heterogeneous in nature, therefore the behaviour of petrophysical properties of these reservoirs is a highly nonlinear. There is no close conventional statistical model can describe the behaviour of the relation between cementation factor and rock properties. Artificial Neural Network technique is used in many applications to predict variable that usually cannot be measured in linear modelling. Depending on well logs data, the Interactive Petrophysics software had been used to calculate the petrophysical properties of studied oilfield. In this study, the data sets used for training and testing neural network are provided from well number three of Nasiriya oilfield in the south of Iraq. The neural network model was trained using two different training algorithms; Gradient Descent with Momentum and Levenberg - Marquardt. Porosity, permeability and resistivity formation factor relationships to cementation factor are proposed using artificial neural network model. An efficient performance of excellent prediction of cementation factor has been obtained with less than (1*10-4) mean square error (MSE)

Optimized Artificial Intelligence Model for DDoS Detection in SDN Environment

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

Direct contact evaporation of a single two-phase bubble in a flowing immiscible liquid medium. Part I: two-phase bubble size

The evaporation of a single n-pentane drop in another warm flowing liquid (water) medium has been studied experimentally. A Perspex column with an internal diameter of 10 cm and height of 150 cm was used throughout the experiments. N-pentane liquid at its saturation temperature and warm flowing water with flow rate of 10, 20, 30 and 40 L/h were employed as the dispersed and continuous phases, respectively. The active height of the continuous phase in the column (i.e. the level of the continuous phase in the column) covered only 100 cm of the column’s height. A Photron FASTCAM high-speed camera (~ 65,000 f/s) was used to film the evaporation of the drop, while AutoCAD was used to analyse the images from the camera. The diameter ratio (diameter of growing two-phase bubble to initial drop diameter) of the two-phase bubble formed because of the evaporation of the pentane drop in direct contact with the water was measured. Also, the vaporisation ratio (x), the open angle of vapour (β), the total height for complete evaporation and the total evaporation time were measured. The effects of the continuous phase flow rate and the temperature difference between the contacting phases, in terms of Jakob number (Ja), on the measured parameters were investigated. Furthermore, a statistical model to fit the experimental data was developed. The experimental results showed that the diameter of the two-phase bubble is strongly influenced by varying the continuous phase flow rate. The final size of the evaporated vapour bubble was unaffected by the flow rate of the continuous phase, while both the total height required for complete evaporation and hence the time required was significantly influenced. A similar impact was observed for the vaporisation ratio and the open angle of vapour