IoT-Based Big Data: From Smart City towards Next Generation Super City Planning

M. Mazhar Rathore, Anand Paul, Awais Ahmad and Gwanggil Jeon
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M. Mazhar Rathore: School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
Anand Paul: School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
Awais Ahmad: School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea
Gwanggil Jeon: Department of Embedded Systems Engineering, Incheon National University, Incheon, South Korea

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 1, 28-47

Abstract: Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things (IoT) devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and communicating with each other over the Internet to establish smart systems, a huge amount of data, termed as Big Data, is being generated. It is a challenging task to integrate IoT services and to process Big Data in an efficient way when aimed at decision making for future Super City. Therefore, to meet such requirements, this paper presents an IoT-based system for next generation Super City planning using Big Data Analytics. Authors have proposed a complete system that includes various types of IoT-based smart systems like smart home, vehicular networking, weather and water system, smart parking, and surveillance objects, etc., for dada generation. An architecture is proposed that includes four tiers/layers i.e., 1) Bottom Tier-1, 2) Intermediate Tier-1, 3) Intermediate Tier 2, and 4) Top Tier that handle data generation and collections, communication, data administration and processing, and data interpretation, respectively. The system implementation model is presented from the generation and collection of data to the decision making. The proposed system is implemented using Hadoop ecosystem with MapReduce programming. The throughput and processing time results show that the proposed Super City planning system is more efficient and scalable.

Date: 2017
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