The life development and increase the number of internet users imposed an increase in data circulating on the internet network and then make the network more congestion. As a result of all this, some problems arose such as time delay in packets delivery, loss of packets, and exceed the buffer capacity for the middle routers. To overcome those problems, transmission control protocol and active queue management (TCP/AQM) have been used. AQM is the main approach used to control congestion and overcome those problems to improve network performance. This work proposes to use the proportional-integral (PI) controller with a genetic algorithm (GA) as an active queue manager for routers of the Internet. The simulation results show a good performance for managing the congestion with using proportional-integral genetic algorithm (GA-PI) controller better than the PI controller.

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Proportional-integral genetic algorithm controller for stability of TCP network

Active queue management (AQM) is a congestion control mechanism for the early notification of the incipient congestion pursued by dropping/marking packets. The novelty in our result is that we are able to consider heterogeneous traffic (i.e., multiclass traffic) where each class has its own packet discarding policy, queue size, and bandwidth share. Then, this is so far the first control theory-based approach for the AQM problem on the TCP/IP routers that allows multiclass AQM. Our proposed technique assumes that each class has already a controller, designed a priori, and focuses on designing a static state feedback controller where the proposed design approach is based on the solution of linear matrix inequalities (LMIs). From the theoretical conditions, a new controller design methodology for discrete time systems with multi-delays, saturated inputs, and disturbances is proposed to overcome the obstacle of bilinearity which leads to a quite simple LMI condition that is numerically tractable with any convex optimization algorithm. A numerical example is provided at the end of this paper to show the effectiveness and performances of the proposed approach in the presence of multiclass traffic.

AQM Congestion Controller for TCP/IP Networks: Multiclass Traffic

In this work, we used a new approach as active queue management (AQM) to avoid data congestion in TCP/IP networks. The new approach is PSO-PI controller which use the proportional-integral controller as a control unit and particle swarm optimization (PSO) algorithm as an optimization technique to improve the performance of the PI controller and therefore improving the performance of TCP/IP networks as a required goal. The optimization control (PSO-PI) is characterized by access to design and choosing the optimal parameters of  (K_1 and K_p)  to reach optimal solutions in a short way (fewer iterations). The implementation of the PSO algorithm is achieving by using the mathematical system model and M-file and SIMULINK in Mathlab program. Simulation results show good congestion management performance with PSO-PI controller better than the PI controller as AQM in TCP networks, and the proposed method was very fast and required few iterations.

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Design and implementation of a stability control system for TCP/AQM network

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Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review. 

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As the Bitcoin keeps increasing in value compared to other cryptocurrencies, more attention has been given to Blockchain Technology (BT) which is the infrastructure behind the Bitcoin, especially on its role in addressing the problems of the classical centralized system. As a digital currency, Bitcoin is dependent on the decentralized cryptographic tools and peer-to-peer system. The digital currency implements a distributed ledger using Blockchain when verifying any type of transaction. In this paper, the aim is to describe how digital currency networks such as Bitcoin provides a “trust-less” platform for users to embark on money transfers without necessarily depending on any central trusted establishments such as payment services or financial institutions. Furthermore, this work comprehensively overviewed the basic principle that underly BT, such as transaction, consensus algorithms, and hashing. This study also provided a novel classification for blockchain types according to their system architecture and consensus strategy. For each type, our contribution was provided with an example which clearly describes the blockchain features and the transaction steps. Our classification intended to help researchers understand and choose the blockchain for their application. The paper ends with the discussion of the differences between each type

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The road to the blockchain technology: Concept and types

Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the   the network’s state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length.

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Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance

In this paper, a low-cost glove has been manufactured to monitor and analyse the hand motion for the children who suffer from the cerebral palsy. Cerebral palsy (CP) is a combination of continual disorders affect the movement’s evolution due to a non-gradual disturbance in developing fetal or infant cerebrum. An Arduino Nano microcontroller with flex and force sensors are attached to soft cloth glove to form the analysis glove. The data of this study is collected from children who have cerebral palsy, non-cerebral palsy, and children who are treating by physiotherapy and then compared with each other. The results show that the analysis glove helps the physiotherapist to assess the hand functioning problem such as difficulty in hand grip and inability to fully bend the hand figures in general and thumb figure in particular. These remarks can help physiotherapists to define the required program to improve these functions and indications.

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Low power glove for hand functioning analysis in children with cerebral palsy

The feature selection approach (FSA) has lately emerged as a critical method for enhancing accuracy in several disciplines, including e-learning. Since the emergence of the COVID-19 pandemic, the education system in Iraq has diversified its methods to cope with the circumstances, and thus, the positive and negative aspects of e-learning in Iraq are revealed. In this study, we developed a hybrid model that predicts how to rely on a blended learning system during and after the COVID-19 pandemic. A hybrid model was implemented in MATLAB environment that suggested to optimize the support vector machine algorithm in blended e-learning. This hybrid model combines neighborhood component analysis and chi-square statistic. The proposed model demonstrated that platform influence is the most important factor in the e-learning field. It includes high-ranking features, such as adequacy of the virtual environment, a platform that provides sufficient tools for the user, user interest to learn new platforms, obligating students to attend, and having computer skills. The current work was compared with [11], which employed the SVM classification method, using the same datasets. The prediction accuracy of proposed work was better than that of the previous finding. The main goal of proposed work is to obtain a better optimization model than pervious finding by decreasing model error. The Error results was successfully reduced to 0.00012294.

Nada Mahdi Kaittan

Papers

The road to the blockchain technology: Concept and types

Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance

Low power glove for hand functioning analysis in children with cerebral palsy

Feature Selection and Parameter Optimization of SVM by Using a Hybridization Model