End-to-end congestion control in wireless mesh networks using a neural network

Abstract

Maintaining the performance of reliable transport protocols, such as TCP, over wireless mesh networks is a challenging problem due to the unique characteristics of wireless mesh networks such as the lossy nature of the communication medium, absence of a base station, similarity in traffic pattern experienced by neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional TCP variants over wireless mesh networks is that the congestion control mechanisms in conventional TCP variants do not explicitly account for these unique characteristics. To address this problem, this paper proposes a novel neural network based congestion control technique for reliable data transfer over wireless mesh networks. We analyze the proposed congestion control technique in detail and incorporate it into TCP to create a variant that we name intelligent TCP or iTCP. We evaluate the performance of iTCP using ns-2 simulations. Our results demonstrate that our proposed congestion control technique exhibits a significant improvement in total network throughput and average energy consumption per bit compared to congestion control techniques used in other variants of TCP.