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The major reason for the evolution of data is the Applications of Smart City which generates data from IoT dependent Sensors. Smart Devices are connected with other devices and give the perceptions on “How humans use smart devices in aggregation with one another and discretely; also how Smart Devices can be further enhanced for better service” (Ahad et al., 2020;H. Li et al., 2018;Xu et al., 2014). It is strenuous for Traditional methods to offer personalized solutions to users. AI-based Cognitive Computing has sparkled recently in the industry and research community (Gudivada, 2016). Cognitive Computing is used for training the Artificial Intelligence to function the system as “human-brain-like-thinking”.
For improved and Sustainable Smart EcoSystem, MEC and Fog Computing are two unique newfangled solutions that offer faster and enhanced service delivery for cloud users (Ullah et al., 2020). In the beginning, MEC and Fog Computing were adopted to storage facilities and offload cloud computations that are related to the smart device users for pre-processing the data. Later it moved forward to data and service provision. Fog nodes performed as both servers and clients, hence these nodes obtain service from the cloud or offers service to IoT dependent devices (Chiang & Zhang, 2016). Usually, Sensed data are sent by IoT devices and IoT Services, also these data are directly obtained from the Cloud.
Artificial Intelligence dependent Cognitive Computing trained various applications of Smart City and scientific experiments. It provides rapid, better and accurate decisions. Massive amount of data have raised due to the emergence of data from massive amount of sensors in Smart Environments and the amount of data will raise with time. The limitation of the applications of Smart City is addressed below.
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In the environment of Large_Scale_Smart_City, its necessary to understand the structure of Cognitive based system. Here the growth of data is increasing every second and Scalability is also an issue.
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Recent Cognitive Computing Systems are difficult to offer solutions for applications of Multiple Smart City. Flexibility, Scalability and Security are the primary issues in Cognitive Smart City Network (CSC-Net).
This research article aims to provide a solution of flexibility, security and scalability of data in Cognitive Smart City Network (CSC-Net). Now Industries and Research Sectors recognized that Cognitive Computing is the latent service in this fast-growing world. The massive data streams from Smart Cities help to convert the existing solutions of cognitive computing into more effective, efficient and better results. The main goal of this research article are listed below:
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The proposed Cognitive Smart City Network (CSC-Net) architecture establishes the various research areas of Smart City networks such as smart transportation, smart home, smart industry, smart building and smart energy and shares similar idea of cognitive dataset helps to construct multiple applications in Cognitive Computing.
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Applications permit Cognitive Smart City Network (CSC-Net) includes AI dependent Cognitive Computing and Big data-dependent Cognitive Computing.
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Collaborative dependent Intrusion Detection System (C-IDS) with three classifiers are developed. Cloud and Fog Computing adopted the C-IDS technique, hence the intrusion recognition, detection and classification are performed individually at Cloud and Fog Layers respectively.