An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu, Qing; Kosarirad, Houman; Meisami, Sajad; Alnowibet, Khalid Abdulaziz; Hoshyar, Azadeh Noori

doi:10.3390/pr11041162

Cited by 23 publications

(9 citation statements)

References 53 publications

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“…Efficient task scheduling in fog and cloud environments is essential for ensuring the productivity and efficacy of IoT systems. In this vein, Liu Q. et al [7] devised the hybrid meta-heuristic (MH) algorithm to proficiently allocate and schedule tasks across the fog cloud system, with the primary criterion being the minimisation of the makespan. The evaluation of the optimal virtual machine (VM) for task allocation is based on this metric.…”

Section: The Task Scheduling and Resource Optimisation In Iot Networkmentioning

confidence: 99%

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Almudayni,

Soh,

2024

Future Internet

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The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA’s efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.

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Section: The Task Scheduling and Resource Optimisation In Iot Networkmentioning

confidence: 99%

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Almudayni,

Soh,

2024

Future Internet

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“…Through the presented strategy, every village learns from its neighbors rather than from all of its associates. In [28], a hybrid meta-heuristic (MH) algorithm has been proposed to plan the IoT needs in IoT-fog-cloud systems by utilizing an Aquila Optimizer (AO) and AVOA termed AO_AVOA.…”

Section: Related Workmentioning

confidence: 99%

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

Almasoud,

Maray,

Alkahtani

et al. 2024

IEEE Access

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Gastrointestinal (GI) cancer comprises esophageal, gastric, colon and rectal tumors. The diagnosis of GI cancer often relies on medical imaging modalities namely magnetic resonance imaging (MRI), histopathological slides, endoscopy, and computed tomography (CT) scans. This provides particular details about the size, location, and characteristics of tumors. The high death rate for GI cancer patients shows that it is possible to increase analysis for a more personalized therapy strategy which leads to improved prognosis and few side effects although many extrapolative and predictive biomarkers exist. Gastrointestinal cancer classification is a challenging but vital area of research and application within medical imaging and machine learning. Artificial intelligence (AI) based diagnostic support system, especially convolution neural network (CNN) based image examination tool, has enormous potential in medical computer vision. The study presents a GI Cancer Detection and Classification utilizing the African Vulture Optimization Algorithm with Transfer Learning (GICDC-AVOADL) methodology. The major aim of the GICDC-AVOADL model is to examine GI images for the identification of cancer. To achieve this, the GICDC-AVOADL method makes use of an improved EfficientNet-B5 method to learn features from input images. Furthermore, AVOA is exploited for optimum hyperparameter selection of the improved EfficientNet-B5 method. The GICDC-AVOADL technique applies dilated convolutional autoencoder (DCAE) For GI cancer detection and classification. A complete set of simulations was conducted to examine the enhanced GI cancer detection performance of the GICDC-AVOADL technique. The extensive results inferred superior performance of the GICDC-AVOADL algorithm over current models.

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“…However, applying DL methods to personal healthcare is not a straightforward process as it has the potential to alter existing healthcare practices. The method uses behavioral data to facilitate personalized patient care and digital healthcare within the I-fog-cloud network [116]. Also, incorporating advanced deep learning vision-computing techniques within a cognitive cloud framework, this research contributes to the intersection of AI, ML, and IoT in waste management, promoting a healthier environment and sustainable practices [117].…”

Section: Inferring Personal Health Conditions By Wearable Devicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Cited by 23 publications

References 53 publications

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

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