Cloud Service Negotiation in Internet of Things Environment: A Mixed Approach

Zheng, Xianrong; Martin, Patrick; Brohman, Kathryn; Xu, Li Da

doi:10.1109/tii.2014.2305641

Cited by 103 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloud can provide resources, such as storage, processing and communication support, to IoT for compensating its technological constraints. On the other hand, cloud can be applied to more real-world cases because of IoT applications (Jiang et al , 2014; Pan et al , 2015; Kelaidonis et al , 2017; Stergiou, Psannis, Gupta and Ishibashi, 2018; Stergiou, Psannis, Kim and Gupta, 2018; Xie et al , 2017; Zheng et al , 2014).…”

Section: Artificial Intelligence Powered Internet Of Thingsmentioning

confidence: 99%

Artificial Intelligence powered Internet of Things and smart public service

Kang

et al. 2019

LHT

View full text Add to dashboard Cite

Purpose The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China. Design/methodology/approach This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen. Findings The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications. Originality/value This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

show abstract

Section: Artificial Intelligence Powered Internet Of Thingsmentioning

confidence: 99%

Artificial Intelligence powered Internet of Things and smart public service

Kang

et al. 2019

LHT

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a virtual technological platform that allows the storage of files, information, and programs on the Internet, in the so-called "cloud", eliminating the need for storage on local computers [2,94]. This system offers high performance and low implementation cost, enabling the storage of large amounts of data in many cases [3,95].…”

Section: Cloud Computingmentioning

confidence: 99%

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

dos Santos Filho,

de Resende,

Pontes

2024

Future Internet

View full text Add to dashboard Cite

This study aims to develop a theoretical model for digital risks arising from implementing Industry 4.0 (represented by the acronym TMR-I4.0). A systematic literature review was initially conducted using the Methodi Ordinatio methodology to map the principal dimensions and digital risks associated with Industry 4.0 in order to achieve this objective. After completing the nine steps of Methodi, a bibliographic portfolio with 118 articles was obtained. These articles were then subjected to content analysis using QSR Nvivo® version 10 software to categorize digital risks. The analysis resulted in the identification of 9 dimensions and 43 digital risks. The categorization of these risks allowed the construction of maps showing the digital risks and their impacts resulting from the implementation of Industry 4.0. This study advances the literature by proposing a comprehensive categorization of digital risks associated with Industry 4.0, which resulted from an exhaustive literature review. At the conclusion of the study, based on the proposed Theoretical Risk Model for Digital Risks arising from the implementation of Industry 4.0, a research agenda for future studies will be proposed, enabling other researchers to further explore the landscape of digital risks in Industry 4.0.

show abstract

“…Unfortunately, IoT devices do not have enough computing resources to process huge amount of data. Therefore, cloud computing is usually involved and relied on to solve the drawbacks limiting IoT [6]. It has been recognized as a success factor for IoT applications due to its uses for storage, analysis and processing of data.…”

Section: Introductionmentioning

confidence: 99%

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021

IJETER

View full text Add to dashboard Cite

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

show abstract

Cloud Service Negotiation in Internet of Things Environment: A Mixed Approach

Cited by 103 publications

References 28 publications

Artificial Intelligence powered Internet of Things and smart public service

Artificial Intelligence powered Internet of Things and smart public service

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

Contact Info

Product

Resources

About