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Application of Internet of Things in the Health Sector:Toward Minimizing Energy Consumption

Big Data Mining and Analytics 2022年第4期5卷 302-308页

作者： Mohammed Moutaib Tarik Ahajjam Mohammed Fattah Yousef Farhaoui Badraddine Aghoutane moulhime el bekkali IMAGE Laboratory Moulay Ismail UniversityMeknes 50000Morocco Department of Computer Science Faculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco IA Laboratory Faculty of ScienceMoulay Ismail UniversityMeknes 50000Morocco Sidi Mohamed Ben Abdellah University Fes 30000Morocco

The Internet of Things(IoT)is currently reflected in the increase in the number of connected objects,that is,devices with their own identity and computing and communication *** is recognized as one of the most critical areas for future technologies,gaining worldwide *** applies to many areas,where it has achieved success,such as healthcare,where a patient is monitored using nodes and lightweight ***,the powerful functions of IoT in the medical field are based on communication,analysis,processing,and management of data autonomously without any manual intervention,which presents many difficulties,such as energy ***,these issues significantly slow down the development and rapid deployment of this *** main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel *** distance between nodes is one of the factors influencing energy *** this article,we have proposed direct communication between nodes to avoid collision domains,which will help reduce data *** results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

关键词： Internet of Things(IoT) energy consumption cloud computing data storage

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Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

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Intelligent and Converged Networks 2023年第4期4卷 273-279页

作者： Saad-Eddine Chafi Younes Balboul Mohammed Fattah Said Mazer moulhime el bekkali the Laboratory of Artificial Intelligence Data Sciences and Emerging SystemsNational School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco the LIA Laboratory Moulay Ismail UniversityMeknes 50050Morocco

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization *** Algorithm(GA)is widely popular due to its logical approach,broad applicability,and ability to tackle complex issues encountered in engineering ***,GA is known for its high implementation cost and typically requires a large number of *** the other hand,Particle Swarm Optimization(PSO)is a relatively new heuristic technique inspired by the collective behaviors of real *** GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches *** they are often seen as competitors,their efficiency heavily relies on the parameter values chosen and the specific optimization problem at *** this study,we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud *** extensive experiments and performance evaluations,the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim *** comparative analysis sheds light on the strengths and limitations of each algorithm,providing valuable insights for researchers and practitioners in the field.

关键词： particle swarm optimization genetic algorithm performance evaluation edge and fog cloud FogWorkflowSim

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