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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems Engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq raed alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network

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Computers, Materials & Continua 2021年第7期68卷 941-960页

作者： raed alsaqour Saif Kamal Maha Abdelhaq Yazan Al Jeroudi Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia Department of Computer Technology Engineering Iraq University College(IUC)BasraIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University84428RiyadhSaudi Arabia Department of Mechanical Engineering International Islamic University MalaysiaJalan Gombak53100SelangorMalaysia

Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable *** that nodes are highly mobile,MANET’s topology often *** changes increase the difficulty in finding the routes that the packets use when they are *** study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol *** GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their *** GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function *** results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network ***,GALAR was compared to other algorithms in terms of different network evaluation *** GALAR algorithm significantly outperformed the other algorithms that were used in the study.

关键词： Mobile ad hoc network location-aided routing protocol location information genetic algorithm

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Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

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Computers, Materials & Continua 2021年第11期69卷 2313-2337页

作者： Mahmoud Alawi raed alsaqour Abdi Abdalla Maha Abdelhaq Mueen Uddin Department of Computer Engineering and IT Faculty of EngineeringZanzibar UniversityZanzibarTanzania Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Electronics and Telecommunications Engineering College of ICTUniversity of Dar es SalaamTanzania Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Science Universiti Brunei DarussalamNegara Brunei Darussalam

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular *** scenario affects the quality of service(QoS)of vehicle and non-vehicle ***,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly ***-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A ***,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult *** condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of *** studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading ***,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is *** present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading *** proposed mechanism is decentralized,where each vehicle makes the prediction by *** characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several *** MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading rat

关键词： Vehicular network markov predictor 4G LTE-A Wi-Fi offloading prediction model heterogeneous network

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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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Computer Systems Science & Engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq raed alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

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Computers, Materials & Continua 2021年第8期68卷 2377-2397页

作者： Mueen Uddin M.S.Memon Irfana Memon Imtiaz Ali Jamshed Memon Maha Abdelhaq raed alsaqour Faculty of Science Universiti Brunei DarusslamGadongBE1410Negara Brunei Darussalam CSE Department QUESTNawabshahPakistan School of Computing and Mathematics Ulster UniversityJordanstown CampusNewtownabbeyBT370QBUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients ***,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital ***:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR ***:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger *** patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric ***:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the *** proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare *** ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

关键词： Electronic health records blockchain hyperledger fabric patient data privacy private permissioned blockchain healthcare ecosystem

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Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

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Computers, Materials & Continua 2023年第1期74卷 1693-1708页

作者： V.Sridhar K.V.Ranga Rao Saddam Hussain Syed Sajid Ullah Roobaea Alroobaea Maha Abdelhaq raed alsaqour ECE Department Vidya Jyothi Institute of TechnologyIndia CSE Department Neil Gogte Institute of TechnologyIndia School of Digital Science Universiti Brunei DarussalamJalanTungku LinkGadong BE1410Brunei Darussalam Department of Information and Communication Technology University of Agder(UiA)N-4898 GrimstadNorway Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourahbint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network *** the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are *** by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression *** on the classification,resource and trust-aware devices are selected for *** of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling *** simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.

关键词： Mobile network multivariate renkonen regression weighted preference bootstrap aggregation resource-aware secure data communication NOMA

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Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

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Computer Systems Science & Engineering 2024年第5期48卷 1133-1147页

作者： raed Alotaibi Omar Reyad Mohamed Esmail Karar Applied College Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Electronic Engineering Menoufia UniversityMenouf32952Egypt

E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in *** article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning *** proposed e-learning analytics system includes a new deep forest *** consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural *** developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final *** have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 *** proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous *** allows academic advisors to support expected failed students significantly and improve their academic level at the right ***,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

关键词： E-learning behavior data student evaluation artificial intelligence machine learning

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Recent progress in two-dimensional metallenes and their potential application as electrocatalyst

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Journal of Energy Chemistry 2024年第7期94卷 577-598页

作者： Umer Shahzad Mohsin Saeed Muhammad Fazle Rabbee Hadi M.Marwani Jehan Y.Al-Humaidi Muhammad Altaf raed H.Althomali Kwang-Hyun Baek Md.Rabiul Awual Mohammed M.Rahman Department of Chemistry Faculty of ScienceKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Biotechnology Yeungnam UniversityGyeongsanSouth Korea Center of Excellence for Advanced Materials Research(CEAMR) King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Chemistry College of SciencePrincess Nourah Bint Abdulrahman UniversityP.O.BOX 84428Riyadh 11671Saudi Arabia Department of Chemistry Center of Excellence for Innovation in Chemistry(PERCH-CIC)Center of Excellence in Materials Science and TechnologyFaculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Department of Chemistry College of Art and SciencePrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir 11991Saudi Arabia Western Australian School of Mines:Minerals Energy and Chemical EngineeringCurtin UniversityGPO Box U 1987PerthWA 6845Australia

In this article,we looked at metallenes,a novel class of two-dimensional(2D)metals that are attracting interest in the energy and catalysis *** is one area where their exceptional physicochemical and electrical characteristics might be *** are unique because they include several metal atoms that are not in a coordinated *** makes them more active and improves their atomic uti-lization,which in turn increases their catalytic *** article delves into the potential of two-dimensional metals as electrocatalysts for carbon dioxide reduction,fuel oxidation,oxygen evolution,and oxygen reduction reactions in the context of sustainable energy *** to the exception-ally high surface-to-volume ratio,large surface area as well as their optimized atomic use efficiency,2D materials defined by atomic layers are crucial for surface-related sustainable energy *** to its exceptional properties,such as high conductivity and the ability to enhance the exposure of active metal sites,2D metallenes have recently attracted a lot of interest for use in catalysis,electronics,and energy-related *** their highly mobility,adjustable surface states,and electrical struc-tures that can be fine-tuned,2D metallenes are promising nanostructure materials for use in energy con-version with the sustainable applications.

关键词： Metallenes Electrocatalysts Electrochemical processes Atomically thin structure

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Cognitive Radio Networks Using Intelligent Reflecting Surfaces

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Computer Systems Science & Engineering 2022年第11期43卷 751-765页

作者： raed Alhamad Information Technology Department Saudi Electronic UniversityRiaydhKingdom of Saudi Arabia

In this article,we optimize harvesting and sensing duration for Cognitive Radio Networks(CRN)using Intelligent Reflecting Surfaces(IRS).The secondary source harvests energy using the received signal from node ***,it performs spectrum sensing to detect Primary Source PS *** PS activity is not detected,The Secondary Source SS transmits data to Secondary Destination SD where all reflected signals on IRS are in phase at *** show that IRS offers 14,20,26,32,38,44,50 dB enhancement in throughput using M=8,16,32,64,128,256,512 reflectors with respect to CRN without *** also suggested to add a second IRS between A and SS to increase the harvested *** use of 2 IRS with M1=8 reflectors in the first IRS and M2=8 reflectors in the second IRS offers 18 dB gain(respectively 32 dB)gain with respect to a single IRS with M2=8 reflectors(respectively without IRS).The use of 2 IRS with M1=16 reflectors in the first IRS and M2=8 reflectors in the second IRS offers 28 dB gain(respectively 42 dB)gain with respect to a single IRS with M2=8 reflectors(respectively without IRS).Our results are valid for Nakagami channels of fading figure *** also provide the throughput of IRS with energy *** have studied packet waiting time and total delay in the presence and absence or *** Signal to Noise Ratio(SNR)per bit equal to 0 dB,packet waiting time is 0.9 ms when there is no IRS and 0.5 ms when there is an IRS with M=8 *** SNR per bit equal to 0 dB,total transmission delay is 54 ms when there is no IRS and 1.5 ms when there is an IRS with M=8 *** show that the energy efficiency is larger when both harvesting and sensing durations are *** maximum of energy efficiency is 260 Mbit/s/Hz/J when harvesting and sensing durations are optimized while the maximum is 80 Mbit/s/Hz/J when harvesting and sensing durations are not optimized.

关键词： Cognitive radio networks energy harvesting spectrum sensing throughput analysis

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