Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews

Fadziso, Takudzwa; Adusumalli, Harshini Priya; Pasupuleti, Mahesh Babu

doi:10.18034/ajase.v7i1.49

Asian j. appl. sci. eng.

2018

DOI: 10.18034/ajase.v7i1.49

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Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews

Takudzwa Fadziso

Harshini Priya Adusumalli

Mahesh Babu Pasupuleti³

Abstract: An IoT platform is a multi-layer system that enables linked device automation. IoT platforms are enabling software that connects various hardware devices, access points, and networks to other sections of the value chain. Virtual objects are now essential in any IoT platform. In this work, we design and create a cloud-based IoT platform that allows users to register and initialize virtual objects, then consume them via the IoT marketplace and integrate them into IoT applications. The proposed IoT platform diffe… Show more

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Cited by 8 publications

References 16 publications

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Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Chisty¹,

Adusumalli²

2022

ABC j. adv. res.

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Probabilistic intelligence is vital in current management and technology. It is simpler to persuade readers when a management or engineer reports connected difficulties with objective statistical data. Statistical data support the evaluation of the true status, and cause and effect can be induced. The rationale is proven using deductive logic and statistical data verification and induction. Quality practitioners should develop statistical thinking skills and fully grasp the three quality principles: “essence of substance,” “process of business,” and “psychology.” Traditional quality data include variables, attributes, faults, internal and external failure costs, etc., obtained by data collection, data processing, statistical analysis, root cause analysis, etc. Quality practitioners used to rely on these so-called professional qualities to get a job. If quality practitioners do not keep up with the steps of times, quality data collection, organization, analysis, and monitoring will be confusing or challenging. Increasingly, precision tool machines are embedded in various IoTs, gathering machine operation data, component diagnostic and life estimation, consumables monitoring and utilization monitoring, and various data analyses. Data mining and forecasting have steadily been combined into Data Science, which is the future of quality field worth worrying about.

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Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Chisty¹,

Adusumalli²

2022

ABC j. adv. res.

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Data Privacy-Preservation: A Method of Machine Learning

Achar

2018

ABC j. adv. res.

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The privacy-preservation field in cyber security tends to affiliate with the protection measure related to the use of data and its sharing via third parties for activities such as data analysis. The paper's main objective for this research article will be to use machine learning models that tend to aid as a privacy-preservation technique (PPT). The augmentation of machine learning as a technique for privacy preservation has been able to address the challenges facing the current field of cyber security concerning data protection and security. The paper summarizes the methods such as "federated learning" to address the current issue in the network security field relating to data protection. The rise of augmentation of machine learning in privacy preservation is due to the development of cloud-based applications that are usually prone to data protection issues. Thus, the result of machine learning was necessary to counteract data insecurity. However, the use of machine learning in privacy preservation has remained proficient; there still needs to be a literature gap between the theory and the application of machine learning.

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The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

Pasupuleti¹,

Adusumalli²

2021

Asian bus. rev.

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It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding.

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Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews

Cited by 8 publications

References 16 publications

Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Data Privacy-Preservation: A Method of Machine Learning

The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

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