Abstract
Modern smart city projects are evolving to the next level of data-centric situation awareness and decision makings, thereby requiring much intensive data integration over various data sources made from city space. In order to satisfy a variety of data demands for diverse smart city applications, we have been developing an integrated IoT data service, IoTDA, to provide essential data-oriented services from data collecting to deep learning-based data analysis. In this paper, we introduce the overall architecture and functions of the service platform and explain how the platform will be used with a case study of road surface analysis. In particular, we examine how our data service can be connected to public smart city applications and present the common direction that these types of urban data services should provide for advanced city services.
Author Keywords
- IoT Data Service,
- Smart City,
- Deep Learning Service,
- Traffic Data Analysis,
- Visual Data Analysis
IEEE Keywords
- Roads,
- Videos,
- Smart cities,
- Machine learning,
- Data analysis,
- Task analysis,
- Data integration
Introduction
On behalf of the movements towards the next-generation smart city age based on the prevalent fast wireless network, big data processing platforms, and advanced AI technology, every entity in our space are increasingly being connected to each other to share real-world situational data and solve urban problems in much smarter ways. In terms of the cyber-physical systems, real-world space needs to be connected to cyberspace focusing on data, which is usually sensor data reflecting on the situation of real-world space. In general, since such real-world sensing data are generated and collected by means of the IoT technology, in this paper, we call such data by ‘IoT Data’ which would be easily imagined by lots of scenarios using various sensors.
Obviously, the IoT data are now being prevalent due to the dissemination of sensor devices in our daily living space. Interestingly, they are now being strongly requested to integrate for creating novel values. Hence, IoT data integration becomes a critical issue to solve real-world problems, probably assuming that diverse aspects of data would be better than one simple thing. However, it is not a simple task to merge or integrate IoT data from different sources by the reasons as follows; 1) Bottom-up data generation: generally, an IoT data source has its own goal, not considering other uses and 2) Non-trivial cost and efforts to make it ready to integrate them.
In order to overcome the heterogeneity of various IoT data to enable them to be integrable and useful, we have been developing an integrated IoT data service system, IoTDA which can collect various IoT data, make them be inter-connectable, and provide AI-based data analysis service. In the remainder of this paper, section 2 first describes what should be made for IoT data to be ready for uses in various smart city applications. Then, section 3 will introduce the IoTDA system briefly focusing on the major capabilities to collect, preprocess, and analyze heterogeneous IoTDA together. In addition, section 4 will depict a practice of how we make it possible to construct smart city applications. Finally, we conclude the paper and address the future work in section 5.
READY-TO-USE IOT DATA FOR SMART CITY
In this section, we describe practical requirements on IoT data in terms of what should be made for smart city applications. This is a critical issue because most cities have already lots of legacy urban sensing systems from monitoring the atmosphere to road surveillance systems. In order to solve air-quality problems, it is expected to examine if traffic congestion causes particulate matters much more. Furthermore, for better measurement of traffic for every street, city officials increasingly hope to take advantage of AI technologies. In this paper, we focus on the two aspects of requirements to make IoT data be ready for urgent urban problems.
Conclusion
In this paper, we describe the critical requirement of what should be made for IoT to be ready to support various smart city applications. We also introduced our IoT data service platform, IoTDA briefly and showed a case study where we examined road conditions with the help of the platform in terms of road sensing data provision and AI-based road condition analysis. In our future work, we will advance our platform to deal with various urban sensing data for answering the demands from much agile and complex smart city applications.
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:
Making IoT Data Ready for Smart City ApplicationsBibliography
author
Year
2020
Title
Making IoT Data Ready for Smart City Applications
Publish in
2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 2020, pp. 605-608,
Doi
2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 2020, pp. 605-608,
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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
