Abstract Monitoring and troubleshooting a large wireless mesh network (WMN) presents several challenges. Diagnosis of problems related to wireless access in these networks requires a comprehensive set of metrics and network monitoring data. Collection and offloading of a large amount of data are infeasible in a bandwidth constrained mesh network. Additionally, the processing required to analyze data from the entire network restricts the scalability of the system and impacts the ability to perform real‐time fault diagnosis. To this end, we propose MeshMon, a network monitoring framework that includes a multi‐tiered method of data collection. MeshMon, dynamically controls the granularity of data collection based on observed events in the network, thereby achieving significant bandwidth savings and enabling real‐time automated management. Our evaluation of MeshMon on a real testbed shows that we can diagnose a majority (87%) of network faults with a 66% savings in bandwidth required for network monitoring. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless mesh networks (WMNs) provide an attractive method to provide Internet connectivity in developing regions. Traditional mesh routing protocols are designed to find high quality/throughput multihop routes in the network. However, these solutions do not consider constraints imposed by the capacity at the gateway, often the bottleneck in such rural area networks. In this paper, we demonstrate the importance of intelligent choice of gateways in WMNs. We present the design of a new gateway-aware routing metric that picks high throughput routes in the presence of heterogeneous gateways. Our evaluation in simulations as well as on a testbed show significant increase in network throughput.

Rate adaptation is a critical component that impacts the performance of IEEE 802.11 wireless networks. In congested networks, traditional rate adaptation algorithms have been shown to choose lower data-rates for packet transmissions, leading to reduced total network throughput and capacity. A primary reason for this behavior is the lack of real-time congestion measurement techniques that can assist in the identification of congestion-related packet losses in a wireless network. In this work, we first propose two real-time congestion measurement techniques, namely an active probe-based method called Channel Access Delay, and a passive method called Channel Busy Time. We evaluate the two techniques in a testbed network and a large WLAN connected to the Internet. We then present the design and evaluation of Wireless cOngestion Optimized Fallback (WOOF), a rate adaptation scheme that uses congestion measurement to identify congestion-related packet losses. Through simulation and testbed implementation we show that, compared to other well-known rate adaptation algorithms, WOOF achieves up to 300 percent throughput improvement in congested networks.

Traditional rate adaptation solutions for IEEE 802.11 wireless networks perform poorly in congested networks. Measurement studies show that congestion in a wireless network leads to the use of lower transmission data rates and thus reduces overall network throughput and capacity. The lack of techniques to reliably identify and characterize congestion in wireless networks has prevented development of rate adaptation solutions that incorporate congestion information in their decision framework. To this end, our main contributions in this paper are two-fold. First, we present a technique that identifies and measures congestion in an 802.11 network in real time. Second, we design Wireless congestion Optimized Fallback (WOOF), a measurement-driven rate adaptation scheme for 802.11 devices that uses the congestion measurement to identify congestion related packet losses. Through experimental evaluation, we show that WOOF achieves up to 300% higher throughput in congested networks, compared to other well-known adaptation algorithms.