| Title |
Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management |
| ID_Doc |
37203 |
| Authors |
Silva, BN; Khan, M; Han, KJ |
| Title |
Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.future.2017.06.024 |
| Abstract |
Emergence of smart things has revolutionized the conventional internet into a connected network of things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were made to realize the notion of smart cities. However, demands for processing enormous amount of data and platform incompatibilities of connected smart things hindered the actual implementation of smart cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which is further integrated with the web via a smart gateway. Integration with the web provides a universal communication platform to overcome the platform incompatibilities of smart things. We introduced Big Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine the threshold values for intelligent decision-making and to present the performance improvement gained in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT) integrated smart building architecture (smart home) to reveal the performance improvements of the proposed smart city architecture in terms of network performance and energy management of smart buildings. (C) 2017 Elsevier B.V. All rights reserved. |
| Author Keywords |
Smart city; Big Data analytics; Smart home; Web of things; RESTful architecture |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000527331800076 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
|