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您的检索式：作者名="Soha Alhelaly"
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| 1 | Fuzzy Control Based Resource Scheduling in IoT Edge Computing显示文摘Edge Computing is a new technology in Internet of Things(IoT)paradigm that allows sensitive data to be sent to disperse devices quickly and without delay.Edge is identical to Fog,except its positioning in the end devices is much nearer to end-users,making it process and respond to clients in less time.Further,it aids sensor networks,real-time streaming apps,and the IoT,all of which require high-speed and dependable internet access.For such an IoT system,Resource Scheduling Process(RSP)seems to be one of the most important tasks.This paper presents a RSP for Edge Computing(EC).The resource characteristics are first standardized and normalized.Next,for task scheduling,a Fuzzy Control based Edge Resource Scheduling(FCERS)is suggested.The results demonstrate that this technique enhances resource scheduling efficiency in EC and Quality of Service(QoS).The experimental study revealed that the suggested FCERS method in this work converges quicker than the other methods.Our method reduces the total computing cost,execution time,and energy consumption on average compared to the baseline.The ES allocates higher processing resources to each user in case of limited availability of MDs;this results in improved task execution time and a reduced total task computation cost.Additionally,the proposed FCERS m 1m may more efficiently fetch user requests to suitable resource categories,increasing user requirements. | Samah Alhazmi Kailash Kumar Soha Alhelaly | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 2 | Intelligent Microservice Based on Blockchain for Healthcare Applications显示文摘Nowadays,the blockchain,Internet of Things,and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing,and analytics approaches,including improved service platforms.Nevertheless,one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment.Improved data analytics model not only provides support insights in IoT data but also fosters process productivity.Designing a robust IoT-based secure analytic model is challenging for several purposes,such as data from diverse sources,increasing data size,and monolithic service designing techniques.This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment.The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT.To demonstrate the proposed model effectiveness,we have used the IoT fitness application as a case study.Based on the designed predictive analytic model,a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness.Moreover,the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan.Finally,the proposed model is evaluated using Hyperledger Caliper in terms of latency,throughput,and resource utilization with varying peers and orderer nodes.The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios. | Faisal Jamil Faiza Qayyum Soha Alhelaly Farjeel Javed Ammar Muthanna | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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