| Title |
Data Analysis and Outlier Detection in Smart City |
| ID_Doc |
45449 |
| Authors |
Abid, A; Kachouri, A; Mahfoudhi, A |
| Title |
Data Analysis and Outlier Detection in Smart City |
| Year |
2017 |
| Published |
|
| DOI |
|
| Abstract |
A smart city (SC) is a living space requiring technologies such as high-tech information to improve the quality of residents' lifestyle and to optimize, economically and environmentally, the management of available resources such as roads, building activities, environments, lights and water. Wireless Sensor Networks (WSN) highlight an importance for data analysis in smart city to monitoring systems. The challenge is to identify outlier data in order to heighten the QoS. In fact, the use of heterogeneous set of technologies and participants, the deployment of many unsafe nodes and the harsh environment conditions imply the outlier detections algorithms requirement. In this article, we initially describe the SC application in general and for WSN. Then, we propose outlier detection as a solution to have safe SC, powered by WSN using the concepts of Internet of Things and Big Data. |
| Author Keywords |
Smart City; Big data; IoT; Safety Engineering; WSN; Outlier Detection |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000426614300001 |
| WoS Category |
Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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