| Title |
IoT Architecture for Smart Cities Leveraging Machine Learning and SDN |
| ID_Doc |
43787 |
| Authors |
Miladinovic, I; Schefer-Wenzl, S; Hirner, H |
| Title |
IoT Architecture for Smart Cities Leveraging Machine Learning and SDN |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/telfor48224.2019.8971033 |
| Abstract |
Smart cities are expected to offer a variety of innovative digital services and to improve quality of life of their citizens. To achieve this, a huge number of sensors permanently monitors different objects generating vast amounts of data which need to be transmitted and processed efficiently. Today's networks struggle to scale and manage this data. Novel concepts, such a Software Defined Networking (SDN) and Multi-Access Edge Computing (MEC), are introduced to overcome these challenges. In this paper we enhance an IoT architecture for smart cities by integrating SDN for the optimal allocation of smart city applications between MEC centers and the cloud. This decision is taken using machine learning based on historical and current data. Some smart city applications illustrate this approach. |
| Author Keywords |
IoT; Machine Learning; Smart Cities; Multi-Access Edge Computing; SDN |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000568618700040 |
| WoS Category |
Telecommunications |
| Research Area |
Telecommunications |
| PDF |
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