Critical link identification and prioritization using Bayesian theorem for dynamic channel assignment in wireless mesh networks

Abstract

Wireless Mesh Networks (WMN) is a key backhaul technology used in 802.11 networks to provide ubiquitous coverage to isolated areas that require highspeed connectivity. The multi-radio feature of WMN has enabled the mesh routers to derive the full benefits of multiple channels for providing parallel transmissions in a single collision domain. However, co-channel interfering links badly affect the channel capacity and force the mesh routers to switch the radio interface to other less interfering channel. In dynamic channel assignment, if the channel switches occur frequently, the traffic disruptions lead to excessive packet delays and drops. These problems are mostly observed in specific dense areas, where traffic saturation occurs. The existing schemes lack in properly identifying the bandwidth starved links. Therefore, the focus of this paper is to enhance the throughput and minimize the packet drops by critically identifying the bottleneck links and prioritize them for better channel assignments. The proposed metric exploits the statistical inference on dropped packets to determine the effect of interference on the achievable capacity of the links. The traffic load and the effective capacity are collectively used to identify the saturated links. The proposed metric has been evaluated through extensive simulations. The results demonstrate the validation of proposed metric with a considerable increase in performance.