| Title |
Improve IoT Ehealth Ecosystem with SDN |
| ID_Doc |
42227 |
| Authors |
Zemrane, H; Baddi, Y; Hasbi, A |
| Title |
Improve IoT Ehealth Ecosystem with SDN |
| Year |
2019 |
| Published |
|
| DOI |
10.1145/3368756.3368981 |
| Abstract |
Today's world knows a constant development of Smart cites like Amsterdam Smart City, Smart City Lyon and others, the development of these smart cities depends on the development of the Internet of Things, in the perception layer of the Internet of Things architecture, wireless sensor networks can be an important factor to optimize IoT solutions with their high capabilities of physical magnitude detection and there various networks connections. The Internet of Things is a new opportunity for commercial development in several sectors of activity, in our article we focus specially on the heath care field to create an IoT Ehealth ecosystem that can measure record and transmit health information of the patient to the Internet to prevent against any threat that can make us lose our patient. On the next few years the IoT market promises a big development which means a generation of large amount of data and setting up of a lot of network infrastructures, to remedy the problems of the huge consummation of the network resources and for an efficient data management in our IoT Ehealth ecosystem we were based on the Software Define Network technology that decuple the control function present on the Ehealth network equipment and centralized in a single controller, the Ehealth network equipment are thus responsible only for data transmission leaving decision making to the controller. This create a new notion called Software Define Internet of Things Ehealth Ecosystem. |
| Author Keywords |
Internet of Thing (IoT); Eheatlh ecosystem; Software Define Network (SDN); Wireless Sensor Network (WSN); Virtualization |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000526374200015 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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