Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management
Introduction
The emergence of smart things has highly favored the notion of connecting everyday objects via the existing networks. The dramatic increase of smart devices led towards the realization of IoT [1]. The IoT is a continuously growing network that autonomously identifies and shares data among uniquely addressable devices [2], [3]. As the concept matures, IoT notion is diversified into multiple interest areas pioneering numerous applications i.e. smart home, smart city, smart grid, and many more [4], [5], [6], [7]. Initially, smart city concept was introduced to enhance the quality of life of urban citizens by effective utilization of public resources and services [8]. Smart city is built upon the IoT concept including smart community, smart transportation, smart energy, etc. [9]. The urban IoT optimizes transportation, surveillance, healthcare, and energy management with aid of autonomous data collection and data sharing. Accordingly, heterogeneous smart devices should be able to share collected smart city data at the application level despite of the platform variations. Consequently, it creates a significant demand for a common platform that facilitates successful cross platform communication among heterogeneous smart things. However, building a completely new universal platform is rigorous. So that, redefining an existing platform seemed to be the most feasible approach. Thus, academic and industrial experts identified web as a suitable candidate to offer cross platform communication for smart devices included in IoT applications i.e. smart home and smart city [10], [11]. Thereupon, WoT notion was introduced to use web-associated technologies in IoT. In addition to the heterogeneity, smart city realization is further challenged by processing efficacy of enormous amount of data. In order to meet service requests, smart city architecture should allow real-time data processing. Therefore, embedding Big Data analytics to the IoT environment seemed to be an excellent fit that ensures flexible, reliable, and real-time data processing and decision-making [12], [13].
In the recent past, multiple interest groups have focused on improving the usability of smart city architectures. Nevertheless, a majority of the attempts were designed to uplift individual aspects of smart cities i.e. water management, parking management, and so forth [14], [15]. Consequently, the practicability of smart city has been deteriorated due to lack of completeness. Therefore, a complete smart city architecture has become a crucial demand in the modern technological era. Further, heterogeneous smart devices in smart city architecture generates colossal amount of data. The rapid increase of data volumes exponentially degrades the performance of conventional data processing mechanisms. Thus, it is essential to utilize real-time processing mechanisms to make smart cities efficient and responsive [9]. In [8], an urban IoT was implemented to serve city administration tasks. It consists of a data collection system, street light monitoring system, and a gateway. SmartSantander testbed is used in [8] to determine the benefits of embedding Big Data analytics in smart cities. The emergence of wireless sensor networks (WSN) has improved smart city applications with aid of connected sensors. However, the improvements rely on effective communication protocols that consumes less power. Bluetooth, IEEE 802.15.4, IPv6 over low power wireless personal area network (6LoWPAN), and constrained application protocol (CoAP) are low power communication protocols, which are widely accepted for IoT applications [16]. Afore stated protocols serve smart thing integration at the network level. As a result, connecting heterogeneous smart devices at the application level of smart city architecture is hindered due to the platform incompatibilities. So that, integrating a cross-platform communicator into smart city architecture is much required to enable platform independent seamless communication among smart devices. The authors of [17] proposed a conceptual three tier pyramidal architecture with a wireless ubiquitous platform. Even though, above stated studies have addressed vital areas of smart city implementation, a complete smart city architecture capable of making intelligent decisions based on real-time data processing, while ensuring platform independency at the application level is still demanding. Addressing these challenges of smart city application will support to enhance the quality of service provision and efficient energy management of the network and smart buildings.
In this paper, we propose a complete smart city architecture embedded with Big Data analytics. Compared to traditional data analytics, Big Data analytics approach can extract information that is more intelligent, while maximizing data processing speed and improving reliability of decision-making. The urban IoT collects and shares data autonomously. So that, we familiarized the WoT concept into smart city architecture to make smart things accessible and manageable via open web standards. Smart city services are integrated with the web using a smart gateway to allow cross-platform communication at the application level. RESTful application program interfaces (API) expose services to the remote users. Smart home energy management is presented as an example scenario of controlling service operations using RESTful APIs. The proposed scheme achieved efficient home energy management by enabling remote web accessibility to smart objects integrated with WoT. Henceforth, the proposed smart city successfully improves data processing speed, energy management of smart city owing to embedded Big Data analytics and web integration via RESTful smart gateway. The rest of the paper is organized as follow. Section 2 elaborates on the state of the art on smart cities collaborating Big Data analytics and platform compatibility concerns for smart cities. The proposed is presented in Section 3. The results of the study and discussion is given in Section 4. Finally we concluded the paper in Section 5.
Section snippets
Related work
A full-scale smart city architecture benefits researchers and industrial experts in various ways as it covers a variety of research approaches e.g. abstract concepts and complete set of services. In past few decades, many researchers attempted to define a generic architecture for IoT based smart city. Current state of the art presents various experimentation and test bed based smart city architectures that address challenges for a generic architecture.
Many studies proposed the deployment of IoT
Proposed scheme
The proposed smart city architecture is embedded with Big Data analytics to serve real-time data processing and intelligent decision-making. Big Data embedded smart city is further integrated with RESTful WoT to allow accessibility and manageability of smart devices using web standards. Consequently, smart objects become controllable according to remote web requests. This feature has been used to manage energy utilization of smart appliances. A smart home connected to the smart home scenario is
Results and data analysis
In this study, we proposed a smart city architecture that can overcome the existing drawbacks of a smart city in terms of performance efficiency and energy efficiency. Performance improvement of the smart city highly depends on the processing and analysis of data from previous studies that belongs to various fields e.g. transportation, healthcare, community development, etc. Herein, we processed authentic data obtained from various sources to confirm the performance improvement gained through
Conclusion
Transforming a conventional city into a smart city requires compatibility of connected devices, ubiquitous access to urban services and resources, and ability to process voluminous data. Therefore, we proposed a smart city architecture that is capable of processing colossal amount of data with aid of data filtration followed by Big Data analytics using a Hadoop ecosystem. The smart city architecture was further extended with WoT integration to facilitate platform compatibility and ubiquitous
Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2017-0-00770).
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03933566).
This study was supported by the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education,
Bhagya Nathali Silva received the B.S and M.S. degree in Information Technology from Sri Lanka Institute of Information Technology, Colombo, in 2011. She is currently a candidate of School of Computer Science and Engineering in Kyungpook National University, Daegu, Korea. Her area of expertise includes architecture designing for Internet of Things, Machine-to Machine Communication, Cyber Physical Systems, and Communication Protocols, etc.
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Bhagya Nathali Silva received the B.S and M.S. degree in Information Technology from Sri Lanka Institute of Information Technology, Colombo, in 2011. She is currently a candidate of School of Computer Science and Engineering in Kyungpook National University, Daegu, Korea. Her area of expertise includes architecture designing for Internet of Things, Machine-to Machine Communication, Cyber Physical Systems, and Communication Protocols, etc.
Murad Khan received the B.S. degree in computer science from university of Peshawar Pakistan in 2008. He completed his Ph.D. degree in computer science and engineering from School of Computer Science and Engineering in Kyungpook National University, Daegu, Korea. Dr. Khan published over 40 International conference and Journal papers along with two books chapters in Springer and CRC press. He also served as a TPC member in world reputed conferences and as a reviewer in numerous journals such as Future Generation Systems (Elsevier), IEEE Access, etc. In 2016, he was awarded with Qualcomm innovation award at Kyungpook National University for designing a Smart Home Control System. He was also awarded with Bronze Medal in ACM SAC 2015, Salamanca, Spain, on his distinguished work in Multi-criteria based Handover Techniques. He is a member of various communities such as ACM and IEEE, CRC press, etc. His area of expertise includes ad-hoc and wireless networks, architecture designing for Internet of Things, and Communication Protocols designing for smart cities and homes, Big Data Analytics, etc.
Kijun Han received the B.S. degree in electrical engineering from Seoul National University, Korea, in 1979 and the M.S. degree in electrical engineering from the KAIST, Korea, in 1981 and the M.S and Ph.D. degrees in computer engineering from the University of Arizona, in 1985 and 1987, respectively. He has been a professor of School of Computer Science and Engineering at the Kyungpook National University, Korea since 1988.