How network monitoring and reinforcement learning can improve tcp fairness in wireless multi-hop networks

Abstract

Wireless mesh network (WMN) is an emerging technology for the last-mile Internet access. Despite extensive research and the commercial implementations of WMNs, there are still serious fairness issues in the transport layer, where the transmission control protocol (TCP) favors flows with a smaller number of hops to flows with a larger number of hops. TCP unfair behavior is a known anomaly over WMN that attracts much attention in recent years and is the focus of this paper. In this article, we propose a distributed network monitoring mechanism using a cross-layer approach that deploys reinforcement learning techniques (RL) to achieve fair resource allocation for nodes within the wireless mesh setting. In our approach, we deploy Q-learning, a reinforcement learning mechanism, to monitor the dynamics of the network. The Q-learning agent creates a state map of the network based on the medium access control (MAC) parameters and takes actions to enhance TCP fairness and throughput of the starved flows in the network. The proposal creates a distributed cooperative mechanism where each node hosting a TCP source monitors the network and adjusts its TCP parameters based on the network dynamics. Extensive simulation results and testbed analysis demonstrate that the proposed method significantly improves the TCP fairness in a multi-hop wireless environment.