Improving transport layer performance over multi-hop wireless networks by machine learning

The emerging use of multi-homed wireless devices along with simultaneous multi-path data transfer offers tremendous potentials to improve the capacity of multi-hop wireless networks. Concurrent Multi-path Transfer (CMT) over Stream Control Transmission Protocol (SCTP) is a form of reliable multi-path transport layer protocol with unique features that resonate with multi-path nature of the multi-hop wireless networks. The present thesis identifies and addresses some of the challenges of CMT-SCTP over wireless multi-hop networks. One main challenge raised by the multi-hop wireless network for CMT-SCTP is the out-of-order packet arrival. SCTP uses packet sequence number to ensure delivery. As such, the out-of-order packet arrival triggers receive buffer blocking in CMT-SCTP that causes throughput degradation. Another challenge in using CMT-SCTP over multi-hop wireless networks is the unfair resource allocation towards flows coming from farther away hops. The first part of this thesis focuses on integrating machine learning and network coding in CMT-SCTP to resolve the receive buffer blocking problem. Our state-of-the-art scheme uses Q-learning, a form of Reinforcement Learning (RL), to enable the network coding module to adjust to network dynamics. We confirm the veracity of our proposal by a queuing theory based mathematical model. Moreover, the effectiveness of the proposed scheme is demonstrated through simulations and testbed experiments. In the second part of the thesis, we investigate the fairness behavior of CMT-SCTP towards other CMT or non-CMT flows coming from farther away hops on a multi-hop wireless network. We introduce a Q-learning distributed mechanism to enhance fairness in CMT-SCTP. The proposed method uses Q-learning to acquire knowledge about the dynamics of the network. Consequently, the acquired knowledge is used to choose the best action to improve the fairness index of the network. We evaluate our proposal against standard CMT-SCTP and Resource Pool CMT-SCTP (CMT/RP-SCTP). In the third part of this thesis, we apply our findings in the second part to TCP to demonstrate that the benefits of our fairness mechanism can be extended to other transport layer protocols. The findings of this thesis bring us closer to realization of the vast potential of multi-path data transfer over multi-hop wireless networks.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat