on the other hand, a large volume of data is generated from smart city applications such as healthcare, smart transportation, retail industry, and firefighting. their study provides a basic overview, applications and benefits of the use of human data in providing more personalized solutions via cognitive computing based artificial intelligence. the motivation of this paper is to address the issue of scalability and flexibility of data in implementing cognitive computing-based solutions in a smart city environment. in this section, we provide a brief overview of the internet of things, some aspects related to iot architecture, cognitive iot architecture and smart city architecture in the context of iot common to the development of interoperable iot systems. based on zhang research work  developed concepts and examples of applications related to iot in which they propose a cognitive system that allows the cooperation between several devices. 2. we can provide quicker and real-time based solutions to serve the dynamicity of data produced by smart cities efficiently. big data generated here is in the form of both structured and unstructured data. the right side of the brain is concerned with the perceptual view of the brain which aids in providing intuition, visualization and a creative approach to issues. data analysis: data analysis refers to the selection of cognitive traits that are essential to the training of our model. it aids legal organizations with suggestions on how to proceed with a case and is seen as a supportive member of the legal team rather than as a replacement. cognitive data such as brain activity, environment and spatial–temporal data is used to provide real-time analysis of the road and enhance human safety.
they are unable to provide the level of intelligence and personalization which a cognitive computing solution offers. a large amount of image features is required for training the deep learning model, and the mapping data is used in cognitive systems to provide personalized solutions. a large volume of data which is useful for machine learning such as deep learning and reinforcement learning methods in conjunction with cognitive computing can provide solutions. a volume that is known as the amount of data that is generated and stored. data such as gesture recognition and brain activity in the context of the surrounding environment directly affects a firefighter’s ability to approach the incident with full awareness. we identified that data manipulation and data theft are the primary concerns for the cognitive computing layer to function optimally. to support the treatment and restriction of personal information, there is a requirement due to a lot of information on a cognitive internet of things system in which it may be personal data. in the data layer of the ciot-net architecture, we list the various sources from where data can be collected such as brain activity, environment and emotions and the data type such as human voice-tones, facial and vocal expressions which are essential for building cognitive computing-based ai solutions. we discuss related works in the scope of iot, ciot and smart city architectures. ieee commun mag 51:102–111 ammar m, russello g, crispo b (2018) internet of things: a survey on the security of iot frameworks. hum-cent comput inf sci 8:1–13 li c (2015) big data technology and smart city development: a combination of technology and management perspective. jhj and sr defined the overall organization of the manuscript.
to provide such a distributed intelligence, we propose the application of the cognitive iot concept. similarities between the fc paradigm and cognitive iot (cogni-iot) paradigm can be found in terms of deploying distributed intelligence to the edge. in this paper, we propose the use of a cogni-iot platform for a fc industrial application. section ii give details about the fog computing and the required distributed intelligence that should be used for industrial applications, where a large amount of deployed sensors to the edge is required.
this section provides a detailed description of the cogni-iot platform that is required in order to implement different fc industrial solutions. an overall objective of the pdm algorithm is provided as a service that we call it pdm-as-a-service for iot applications. this observation is with respect to the processing units. his expertise is on signal and information processing for communications, networking and computing applications.
in cognitive iot solutions, machine learning needs to take place in an edge computing architecture. edge computing the concept of cognitive iot can integrate, improve performance, and achieve intelligence. ciot is used to analyze the perceived information 5 steps to a cognitive iot application iot refers to the internet of things where a network of objects or devices with embedded technologies communicate with, cognitive computing, cognitive computing, how to create an iot platform, cognitive meaning, data science.
cognitive iot term is not only identified by academia but embraced by industry too. they consider the cognitive iot as the solution to improve the efficiency of the cogni-iot platform provides interactivity and interoperability between devices and flexibility of operation and thus, it is designed in a portable fashion to reduce the failure rate of iot applications, rules engine serves as a powerful tool for bringing cognition to iot. cognitive iot also aims to simplify human-, business intelligence, social network analysis, fundamentals of scalable data science, data mining and data warehousing, ibm watson iot platform node-red, ibm watson iot platform tutorial, what is internet of things?.
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