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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

Digital Communications and networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay networks

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computers, Materials & Continua 2022年第1期70卷 1799-1814页

作者： Haithem Ben Chikha Ahmad Almadhor Jouf University College of Computer and Information SciencesComputer Engineering and Networks DepartmentSakaka72388Kingdom of Saudi Arabia

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single *** this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m *** purpose consists of classifying pairs of users modulations from superimposed *** achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB *** use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations *** simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian *** also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)*** results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 *** addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

关键词： Automatic classification MIMO two-way cognitive relay network Nakagami-m channels superimposed modulations 5G

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A Naive Bayes Classifier for Fault Detection and Classification Using Dimension Reduction Technique 5

A Naive Bayes Classifier for Fault Detection and Classificat...

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5th International Conference in Electronic engineering, Information Technology and Education, EEITE 2024

作者： Shashoa, Nasar Aldian Ambark Abusaeeda, Omar Jomah, Omer S.M. Faneer, Musa Libyan Academy for Postgraduate Studies Department of Electrical and Computer Engineering Tripoli Libya University of Tripoli Department of Computer Networks Tripoli Libya

ISBN: (纸本)9798350372878

Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant Analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach. © 2024 IEEE.

关键词： Damage detection

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A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

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computers, Materials & Continua 2023年第2期74卷 2557-2573页

作者： Nayef Alqahtani Ali Alqahtani Electrical and Computer Engineering Department School of Engineering and Computer ScienceOakland UniversityRochesterMI48326USA King Faisal University Al-Ahsa31982Kingdom of Saudi Arabia Department of Networks and Communications Engineering Najran UniversityNajran61441Kingdom of Saudi Arabia

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid *** synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of ***,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating *** paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman *** is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access *** MATLAB model has been created to implement the proposed method in the presence of Gaussian and *** results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing *** results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

关键词： Distributed generation asynchrophasor kalman filter phasor estimation phasor measurement unit state variables mean square error and signal to noise ratio

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Adaptive Thresholds for Task Offloading in IoT-Edge-Cloud networks

Adaptive Thresholds for Task Offloading in IoT-Edge-Cloud Ne...

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Al-Tuhafi, Ola M. Al-Hemiary, Emad H. Al-Nahrain University Department of Computer Networks Engineering Baghdad Iraq

ISBN: (纸本)9781665493291

The increased number of connected end devices requires carrying the generated loads efficiently. Cloud and edge computing are considered key technologies to overcome the limitation of end devices from many perspectives: computational power, storage capacity, as well as energy consumption. End devices can offload their overflow tasks to the cloud or to the edge for processing, storage, or analysis. This paper proposes an offloading queueing model and an adaptive offloading algorithm to enhance the offloading performance by making efficient offloading decisions. The suggested queueing model considered general service time distributions and offloading decisions are based on a processing time threshold values. The end device is modeled as an M/G/1 queueing system, while the edge node and the cloud are both modeled as M/G/m queueing systems. The proposed adaptive offloading algorithm can dynamically adjust and determine the perfect offloading threshold value that maintains a constant level of load on the resource limited end devices and edge node along with the increased/decreased loads. Simulation results prove the effectiveness of the proposed adaptive offloading algorithm in minimizing the task mean response times and balancing the generated loads efficiently. © 2023 IEEE.

关键词： Energy utilization

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IoT-Aware Real-Time Healthcare Diagnostic Framework For Diabetes Using Wearable Sensors through Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18183-18194页

作者： Farman, Haleem Shahzad, Yasir Jan, Bilal Nasralla, Moustafa M. Sallam, Karam M. Munasinghe, Kumudu Jamalipour, Abbas Prince Sultan University Smart Systems Engineering Lab Department of Communications and Networks Engineering College of Engineering Riyadh Saudi Arabia University of Peshawar Department of Computer Science Pakistan FATA University Department of Computer Science Kohat Pakistan University of Sharjah Computer Science Department Sharjah United Arab Emirates University of Canberra School of Information Technology and Systems ACT2617 Australia The University of Sydney School of Electrical and Information Engineering NSW2006 Australia

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-LSTM) with Deep Reinforcement Learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as Blood Glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of 10 Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters such as blood pressure and body weight. The proposed framework leverages a dataset of 1,830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model's future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient's diabetic condition is categorized using the Surveillance Error Grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly. © 2014 IEEE.

关键词： Deep reinforcement learning

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Actuator and sensor fault isolation in a class of nonlinear dynamical systems

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Journal of Automation and Intelligence 2024年第2期3卷 57-72页

作者： Hamed Tirandaz Christodoulos Keliris Marios M.Polycarpou KIOS Research Center for Intelligent Systems and Networks and the Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault *** paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical *** and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be *** effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

关键词： Actuator and sensor fault isolation Adaptive approximation Observer-based fault diagnosis Reasoning-based decision logic

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An intelligent active probing and trace-back scheme for IoT anomaly detection

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Digital Communications and networks 2024年第1期10卷 168-181页

作者： Luying Wang Lingyi Chen Neal N.Xiong Anfeng Liu Tian Wang Mianxiong Dong School of Computer Science and Engineering Central South UniversityChangsha 410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA Artificial Intelligence and Future Networks Beijing Normal University&UICZhuhai 519087China Department of Information and Electronic Engineering Muroran Institute of TechnologyMuroran 050-8585Japan

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data *** Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

关键词： Anomaly detection Internet of things Integrating data collection Mobile edge users Intelligent

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Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

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computer Systems Science & engineering 2023年第9期46卷 3457-3469页

作者： Sanghyuck Nam Jaehwan Lee Kyoungchan Kim Mingyu Jo Sangoh Park School of Computer Science and Engineering Chung-Ang UniversitySeoul06974Korea Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Qucell Networks Seongnam13590Korea

Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand *** expansion limits exist for traditional standalone storage systems,such as I/O and file system ***,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data *** this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network *** problem hinders deploying distributed storage systems to multiple data centers over *** lowers the scalability of distributed storage systems to accommodate data storage *** paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system *** proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and *** assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance *** paper also conducts simulations to evaluate the proposed and existing distributed storage *** result shows that our proposed method reduced latency by 38%compared to the existing ***,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.

关键词： Distributed system distributed storage distributed computing object storage

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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