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Title: Smart City Operational Platform Ecology (SCOPE) Intelligent Analytical Agent of IoT Cloud for Bigdata Management
Authors: Ahmad, Gulzar
Keywords: Physical Sciences
Computer & IT
Issue Date: 2021
Publisher: National College of Business Administration & Economics, Lahore
Abstract: The inspiration of this proposed model SCOPE is the emerging technologies which are swiftly transforming the world. There are multiple avenues which have covered in this dissertation, primarily starting from smart cities which are the rational target for emerging technologies like cloud computing, Internet of Things (IoT), Big Data to deal with the services and functionality of the smart cities. The proposed model SCOPE is providing a cloud-based ecosystem to develop the provisioning of standard cloud services as well as intelligent and cognitive assistance to deal with variant scenarios and user requests. This model split into two main modules providing ecosystem development, management and evolution in one module while operationalization in the other module using virtualization to make it distributed, independent and connected at the same time. To deal with the big data used for cognitive correlates along with data property clustering to develop the structure of knowledge base. It has also provided machine learning and deep learning using convolution neural networks to make SCOPE autonomous in terms of data manipulation and decision making with minimal human intervention. It is also notable that this proposed model provides learning and analytical capabilities at operational level as well instead of having this provision only at central level due to which every virtual machine / operational node is having optimum autonomy to deal with the localized operations. To validate the proposed model SCOPE, two use-cases related to the problem statement and research have presented. To implement these use-cases various algorithms have been developed during this research using python and subsequent models. The validation results provide significant evidence to support this research objective and proposed model. The areas covered in this dissertation are rich in terms of development and research, therefore, contribution of this research along with the limitations and possible future lines which are not only challenging but promising too.
Gov't Doc #: 28274
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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