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.

We consider the problem of providing ubiquitous yet affordable Internet connectivity to devices at home, at work, and on the move. In this context, we take advantage of two significant technology trends: the commoditization of WiFi WLAN technology and the rapid growth of cellular data services. We propose an architecture called Cool-Tether that harnesses the cellular radio links of one or more mobile smartphones in the vicinity, builds a WiFi hotspot on-the-fly, and provides energy-efficient, affordable connectivity.

The Internet has revolutionized communication, education, commerce and information access for its users worldwide. Unfortunately, the lack of copper/fiber infrastructure in the rural areas of the developing world has prevented a large majority of the human population from reaping the benefits of the Internet. While the number of mobile subscribers in the developing world has more than quadrupled in the last five years, the adoption of the Internet has shown a slow growth pattern. Recently, there has been a growing interest in providing Internet access to rural areas by means of inexpensive long distance WiFi mesh networks. However, the expensive Internet uplink and the difficulty in troubleshooting of WiFi mesh networks has hindered their large scale deployment. In this paper, we propose Cell-Share an architecture that leverages the explosive growth in cellular network penetration in the developing world to provide rural WiFi mesh networks with an on-demand scalable Internet uplink and troubleshooting back-channel using a collaborative mobile phone framework. We implement Cell-Share on Windows Mobile and Android platforms to demonstrate the feasibility of using the infrastructure of cellular data networks to provide a back-channel for network troubleshooting as well as capacity enhancement for rural mesh 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.