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A Wireless Network Selection Scheme based on Machine Learning : 기계 학습 기반 무선망 선택 기법

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dc.description학위논문 (석사)-- 서울대학교 대학원 : 컴퓨터공학부, 2017. 2. 김종권.-
dc.description.abstractToday, more and more public Wi-Fi APs are deployed for user convenience in response to the growing number of smartphone users. However, when both Wi-Fi and LTE interface are on, mobile devices usually select Wi-Fi interface regardless of its connection quality or stability. The blind preference of Wi-Fi interface can degrade user experience called QoE (Quality of Experience).
We present a decision tree based network connection management named SISA (Smart Interface Selection Algorithm) which takes account of user context, contents and network features such as RSSI, Link Speed, TCP throughput and Gateway RTT (RTT to gateway) to efficiently select wireless radio interfaces. We observed the effect of intermittent connection or continuous connection with low quality Wi-Fi AP on the TCP throughput. Through extensive measurements and experiments in real-field, especially public places, we confirmed that the mobile devices embedded our platform outperforms commodity devices for various scenarios. We also discovered that there is no correlation between network features measured in real-field.
We extend the problem to MPTCP (Multipath TCP) that allows the devices like smartphones and tablets to exploit both interfaces concurrently. Similar with phenomenon over SPTCP (Single Path TCP), in real mobile environments where wireless devices abound and quality of active paths changes frequently, it makes Wi-Fi connection affect the MPTCP performance negatively. To solve the problem, we present a novel path management called MPTCP-ML (MPTCP based on Machine Learning). It manages the usage among multiple paths based on decision calculated by machine learning model. We use path quality metrics as inputs for machine learning model. For accurate capturing of path quality, we utilize different quality metrics including signal strength, data rate, TCP throughput, the number of APs on the same and adjacent channel, and RTT (Round Trip Time). We have implemented MPTCP-ML in Android and conducted experiments for various and dynamic mobile environments. The results show that MPTCP-ML outperforms generic MPTCP, especially for mobile environments.
dc.description.tableofcontentsChapter 1. Introduction 1
1.1 Motivation 1
1.2 Background and Related Work 3
1.3 Goal and Contribution 5
1.4 Thesis Organization 7

Chapter 2. Problem Motivation 8
2.1 Problem Analysis 8
2.2 Correleation 12
2.3 Multipath TCP 13

Chapter 3. StreetSmart: Select the Network Best for You 14
3.1 Introduction 14
3.2 Interface Selection Algorithm over SPTCP 16
3.2.1 Decision Tree 16
3.2.2 Learning and Decision Boundary 17
3.2.3 System Design 18
3.3 Performance Evaluation 20
3.3.1 Experiment Setup 20
3.3.2 Application Layer Performance 21
3.3.3 Transport Layer Performance 27
3.4 Summary 29

Chapter 4. Machine Learning-based Path Management for Mobile Devices over MPTCP 30
4.1 Introduction 30
4.2 Path Management Algorithm over MPTCP 32
4.2.1 Random Decision Forests 32
4.2.2 Tree Learning and Modeling 33
4.2.3 System Design 35
4.3 Performance Evaluation 36
4.3.1 Experiment Setup 36
4.3.2 Performance under Static Environment 37
4.3.3 Performance under Mobile Environment 39
4.4 Summary 41

Chapter 5. Conclusion 42
5.1 Research Contributions 42
5.2 Future Research Directions 43

Bibliography 44

Abstract in Korean 46
dc.format.extent1260011 bytes-
dc.publisher서울대학교 대학원-
dc.subjectPublic Wi-Fi-
dc.subjectPath Management-
dc.subjectNetwork Interface-
dc.titleA Wireless Network Selection Scheme based on Machine Learning-
dc.title.alternative기계 학습 기반 무선망 선택 기법-
dc.contributor.affiliation공과대학 컴퓨터공학부-
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