IoT Real-Time Traffic Classification based on Machine Learning Approach

• Main Authors: Meng-Yuan Ye, 葉孟元
• Other Authors: 郭斯彥
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/5h472z

碩士 === 國立臺灣大學 === 電機工程學研究所 === 106 === As the development of Machine type communications (MTC) and advanced cellular network, such as long-term evolution (LTE) and LTE-Advanced (LTE-A), LTE has been proved as a suitable communication protocol for MTC. An enhanced LTE MTC gateway communication architecture has been proposed. Researches show that with the numbers of IoT devices increasing, the uplink congestion problem becomes severe. Although amounts of radio resource allocation schemes have been proposed in order to provide a high-performance effective MTC, radio resource allocation requires protocol modification, which costs social resource. To our knowledge, no paper considers using IoT devices priority and real-time requirement to solve the uplink congestion problem in LTE MTC gateway. In this thesis, we designed a classification system by combing supervised learning and unsupervised learning model. This system named IRTC (IoT real-time traffic classifier) can determine whether IoT devices are in the state with real-time requirement state and then labels them. Experimental result shows that we can determine the state transition within 8.63 to 13.84 packets delay by IRTC