Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities

Nagarajan, Senthil Murugan; Deverajan, Ganesh Gopal; Chatterjee, Pushpita; Alnumay, Waleed S.; Ghosh, Uttam

doi:10.1016/j.scs.2021.102945

Cited by 78 publications

(40 citation statements)

References 36 publications

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“…For example, students can minimize the focus time of electronic screens and use paper-based courseware or writing for course interaction. In this case, smart wearable devices can be suggested to assist the health management of students engaged in online learning ( Nagarajan et al 2021 ). Appropriate scheduling of outdoor activity courses is proposed to relax students’ physical and mental well-being (when outdoor activity is available), thus in turn promoting online learning efficiency and academic performance.…”

Section: Discussionmentioning

confidence: 99%

Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities

Che

2022

Sustainable Cities and Society

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Section: Discussionmentioning

confidence: 99%

Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities

Che

2022

Sustainable Cities and Society

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“…To the best of our knowledge, a hybrid workload-enabled and secure healthcare monitoring sensing framework in a distributed fog cloud network has not been studied yet. The considered problem and system in the present study differ from existing works [1, 2,18,22,[28][29][30][31] in the following way. The proposed work considers the hybrid workloads such as workflow and the fine-grained model and the proposed mathematical model, whereas the study devises the functions and virtual machine aware fog cloud network which was not considered in the existing works.…”

Section: Related Workmentioning

confidence: 97%

“…These studies considered the single constraint during decision in IoMT. The deep convolutional neuron network-enabled healthcare system is suggested in [28][29][30][31]. The goal is to handle multiple objectives such as energy, makespan, and cost of coarse-grained applications in the distributed IoT fog cloud network in the system.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

et al. 2021

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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

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“…Nagarajan et al [35] reported that an Internet of Things-based FoG-assisted cloud network architecture can collect, monitor, and analyze healthcare data from patients, also providing relief measures to the patients requiring immediate assistance. Similarly, the study by Alhussein et al [28] showed that the cloud-based Parkinson's Disease framework can be a useful tool for the detection of that disease.…”

Section: Urban Health Areasmentioning

confidence: 99%

The Adoption of Digital Technologies and Artificial Intelligence in Urban Health: A Scoping Review

et al. 2022

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As more people live in cities, the impact of urban settings on population health has been increasing. One of the main strategies to cope with urbanization is adopting artificial intelligence (AI) and new digital technologies to develop new urban services that improve citizens’ health and well-being. The aim of this study is to review urban interventions and adopting digital technologies and AI-based algorithms to improve population health. A scoping review of the literature was conducted by querying MEDLINE, Web of Science, and Scopus databases. The included studies were categorized into one urban health area, suggested by the WHO, according to the type of intervention investigated. Out of 3733 records screened, 12 papers met all inclusion criteria. Four studies investigated the “outdoor and indoor pollution” area, one “climate change”, one “housing”, two “health and social services” and four “urban transport” areas. Only one article used a comprehensive approach to public health, investigating the use of AI and digital technologies both to characterize exposure conditions to health determinants and to monitor population health effects, while the others were limited to characterizing exposure conditions to health determinants, thus employing a preliminary public health perspective. From this point of view, countries should foster synergy for the development of research on digital technologies to address the determinants of health in the urban context. From a global health perspective, sharing results with the scientific community would also allow other countries to use those technologies that have been shown to be effective, paving the way for more sustainable living conditions worldwide.

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Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities

Cited by 78 publications

References 36 publications

Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities

Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Adoption of Digital Technologies and Artificial Intelligence in Urban Health: A Scoping Review

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