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Smart city article details

Title	Theoretical Design And Experimental Study For Urban Data Management Using Energy-Saved Iot Big Data
ID_Doc	57200
Authors	Jayanthi M.; Pravallika Reddy C.
Year	2020
Published	Lecture Notes in Networks and Systems, 119
DOI	http://dx.doi.org/10.1007/978-981-15-3338-9_33
Abstract	In this study, an experimental study (ESIF) is presented for urban data management and smart city application using energy-saved IoT big data analytics. This study is also used to manage the energy consumption in urban planning and smart city applications. The previous researchers demonstrate that the big data analysis process is more complex. We use different kinds of sensors such as traffic congestion, weather, smart home, water, and vehicular sensor to collect the resource data. The framework is divided into three phases. The first phase is responsible for collecting the data using IoT devices and interconnects with elasticity (energy-saving) manager. The second phase is data handling operation using MapReduce framework. Third phase deals with decision-making which is an important step to classify the events and produce the decisions accurately. To do this, machine learning concept of deep learning algorithms is used and it produces the decisions more effectively. The Hadoop is to provide offline service as well as online services with the help of spark. Therefore, the effectiveness of the model is illustrated in the case of processing time and throughput. © Springer Nature Singapore Pte Ltd 2020.
Author Keywords	Energy-saving; Hadoop and machine learning; IoT; Smart city; Urban area

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