| dc.contributor | 資科系 | |
| dc.creator (作者) | 張宏慶 | |
| dc.creator (作者) | Jang, Hung-Chin | |
| dc.creator (作者) | Tsai, Tsung-Yen | |
| dc.date (日期) | 2021-10 | |
| dc.date.accessioned | 7-Oct-2022 14:41:29 (UTC+8) | - |
| dc.date.available | 7-Oct-2022 14:41:29 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Oct-2022 14:41:29 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/142379 | - |
| dc.description.abstract (摘要) | With the rapid development and progress of the Internet of Things and artificial intelligence, more and more businesses have combined housing with emerging technologies to create smart homes to improve residents` quality of life. Many services similar to the three major application scenarios of 5G will be applied to different smart devices in future smart homes. Therefore, the overall network traffic of smart homes will inevitably increase substantially, making network traffic management in smart homes an issue worthy of in-depth discussion. However, due to the widespread use of network encryption, it is not easy to obtain information from most network application services by decrypting the traffic. It is also difficult to classify various service flows through traditional network traffic classification methods into distinct application categories for management. This research assumes that Internet Service Providers (ISPs) have to manage tens of thousands of smart homes equipped with various kinds of IoT devices. We used software-defined networking (SDN) technology to simulate a multi-tenant smart home environment, simulate different types of smart home service traffic, and use convolutional neural networks (CNN) to classify network traffic. ISP operators can thus set the bandwidth ratio according to the classified service category to effectively improve QoS and user QoE. The experimental results show that the traffic classification accuracy of the CNN model for smart homes can reach 86.5%, which is higher than the general neural network model by 6.5%. | |
| dc.format.extent | 108 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 86-91 | |
| dc.subject (關鍵詞) | 5G; smart home; traffic classification; deep learning; software defined networking | |
| dc.title (題名) | Using CNN to Optimize Traffic Classification for Smart Homes in 5G Era | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/IEMCON53756.2021.9623079 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/IEMCON53756.2021.9623079 | |
