Smart network maintenance in edge cloud computing environment: An allocation mechanism based on comprehensive reputation and regional prediction model

Rui, Lanlan; Yang, Siqi; Gao, Zhipeng; Li, Wenjing; Qiu, Xuesong; Li, Meng

doi:10.1016/j.jnca.2021.103298

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…In particular, machine learning and artificial intelligence have played key roles in advancing this emerging field of computer science and will continue to do so in new and exciting ways [ 57 ]. It is critical for computer scientists to understand common machine learning and AI algorithms that are being used, as well as consider what research is currently being conducted to advance IoT technologies [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Chataut,

Phoummalayvane,

Akl

2023

Sensors

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The Internet of Things (IoT) technology and devices represent an exciting field in computer science that is rapidly emerging worldwide. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. The proliferation of IoT devices coincides with advancements in wireless networking technologies, driven by the enhanced connectivity of the internet. Today, nearly any everyday object can be connected to the network, reflecting the growing demand for automation and efficiency. This paper reviews the emergence of IoT devices, analyzed their common applications, and explored the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Given the rapid pace at which IoT technology is advancing, this paper provides researchers with a deeper understanding of the factors that have brought us to this point and the ongoing efforts that are actively shaping the future of IoT. By offering a comprehensive analysis of the current landscape and potential future developments, this paper serves as a valuable resource to researchers seeking to contribute to and navigate the ever-evolving IoT ecosystem.

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Section: Introductionmentioning

confidence: 99%

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Chataut,

Phoummalayvane,

Akl

2023

Sensors

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show abstract

“…However, the multi-attribute rating algorithm was employed to calculate the trust values, and LSTM was used to find out the trust threshold based on the behaviour changes. Another researcher [139] employed the Long Short-Term Memory (LSTM) approach to identify the history-based sequence of reputation values.…”

Section: Deep Learning Based On Iot Security and Trust Managementmentioning

confidence: 99%

“…Moreover, many techniques are based on the combined datasets as there are no actual datasets based on trust. The authors in [138,139] examined more innovative solution schemes for privacy, and data integrity based on statistical and DL concepts. But such concepts mostly depend on data reputation and privacy and did not discuss the model validation.…”

Section: Deep Learning Based On Iot Security and Trust Managementmentioning

confidence: 99%

IoT trust and reputation: a survey and taxonomy

et al. 2023

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IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices’ features, such as device’s role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.

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“…In Figure 1 , the connection between X -condition cloud generator and Y -condition cloud generator constitutes the corresponding cloud language prediction rules, and its corresponding algorithm is as follows ( 18 ):…”

Section: Mental Health Prediction Model For Community Workers Involve...mentioning

confidence: 99%

Research Into Mental Health Prediction of Community Workers Involved in the Prevention of COVID-19 Epidemic Based on Cloud Model

Huang

2022

Front. Public Health

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To get to know the mental status of community workers involved in the prevention of COVID-19 epidemic, provide them with mental counseling and guidance, and predict their mental health status, a cloud model for the mental health prediction of community workers involved in the prevention of COVID-19 epidemic was constructed in this paper. First, the method to collect data about mental health was determined; second, the basic definition of cloud was discussed, the digital features of cloud were analyzed, and then, the cloud theory model was constructed; third, a model to predict the mental health of community workers involved in the prevention of COVID-19 epidemic was constructed based on the cloud theory, and corresponding algorithm was designed. Finally, a community was chosen as the research object to analyze and predict its mental health status. The research results suggest that the model can effectively predict the mental health status of community workers involved in the prevention of COVID-19 epidemic.

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Smart network maintenance in edge cloud computing environment: An allocation mechanism based on comprehensive reputation and regional prediction model

Cited by 9 publications

References 14 publications

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

IoT trust and reputation: a survey and taxonomy

Research Into Mental Health Prediction of Community Workers Involved in the Prevention of COVID-19 Epidemic Based on Cloud Model

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