Prashanth Aravinda Kumar Acharya

Congestion-Aware Rate Adaptation in Wireless Networks: A Measurement-Driven Approach

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.

Reliable open spectrum communications through proactive spectrum access

Open Spectrum systems offer an attractive solution to the reuse of under-utilized licensed spectrum. Existing proposals take a reactive sense-and-avoid approach to impulsively reconfigure spectrum usage without any knowledge of future dynamics. We propose a proactive spectrum access approach where secondary users utilize past observations to build predictive models on spectrum availability, and intelligently plan channel usage to maximize utilization and minimize disruptions to primary users. Based on the characteristics of TV-broadcast, we develop a simple availability metric and apply a usability filter to eliminate unreliable channels with heavy and frequent appearance of primary users. Our experimental results show that the proactive approach can significantly reduce the number of disruptions. We also observe a clear tradeoff between the disruption rate and the throughput at secondary users. By varying the usability filter threshold, we can to control this tradeoff according to the constraints of primary users and the application requirements at secondary users.

Rate Adaptation in Congested Wireless Networks through Real-Time Measurements

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.

Sticky CSMA/CA: Implicit synchronization and real-time QoS in mesh networks

We propose a novel approach to QoS for real-time traffic over wireless mesh networks, in which application layer characteristics are exploited or shaped in the design of medium access control. Specifically, we consider the problem of efficiently supporting a mix of Voice over IP (VoIP) and delay-insensitive traffic, assuming a narrowband physical layer with CSMA/CA capabilities. The VoIP call carrying capacity of wireless mesh networks based on classical CSMA/CA (e.g., the IEEE 802.11 standard) is low compared to the raw available bandwidth, due to lack of bandwidth and delay guarantees. Time Division Multiplexing (TDM) could potentially provide such guarantees, but it requires fine-grained network-wide synchronization and scheduling, which are difficult to implement. In this paper, we introduce Sticky CSMA/CA, a new medium access mechanism that provides TDM-like performance to real-time flows without requiring explicit synchronization. We exploit the natural periodicity of VoIP flows to obtain implicit synchronization and multiplexing gains. Nodes monitor the medium using the standard CSMA/CA mechanism, except that they remember the recent history of activity in the medium. A newly arriving VoIP flow uses this information to grab the medium at the first available opportunity, and then sticks to a periodic schedule, providing delay and bandwidth guarantees. Delay-insensitive traffic fills the gaps left by the real-time flows using novel contention mechanisms to ensure efficient use of the leftover bandwidth. Large gains over IEEE 802.11 networks are demonstrated in terms of increased voice call carrying capacity (more than 100% in some cases). We briefly discuss extensions of these ideas to a broader class of real-time applications, in which artificially imposing periodicity (or some other form of regularity) at the application layer can lead to significant enhancements of QoS due to improved medium access.

Gateway-aware routing for wireless mesh networks

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.

MARS: Link-layer rate selection for multicast transmissions in wireless mesh networks

IEEE 802.11 devices dynamically choose among different modulation schemes and bit-rates for frame transmissions. This rate adaptation, however, is restricted only to unicast frames. Multicast (and broadcast) frames are constrained to use a fixed low bit-rate modulation, resulting in low throughput for multicast streams. Availability of high bandwidth and efficient use of the medium is crucial to support multimedia multicast streaming applications such as IPTV, especially in multihop mesh networks. To address this problem, we propose a rate adaptation algorithm for multicast transmissions in these networks. The proposed algorithm, MARS, is distributed in nature, and relies on local network measurements to select a transmission bit-rate for a given multicast group. The algorithm also facilitates the joint use of bit-rate selection and link-layer mechanisms such as acknowledgements and retransmissions to improve reliability of high throughput multicast streams. Based on implementation and evaluation on a testbed, the algorithm provides up to 600% gain in throughput compared to traditional 802.11 networks in some scenarios. Additionally, the algorithm can support multicast streams while consuming a small fraction (20%) of the resources compared to the basic 802.11 operation.

Antler: A multi-tiered approach to automated wireless network management

Management of a large scale wireless network, be it an infrastructured WLAN or a metro-scale mesh network, presents several challenges. Troubleshooting problems related to wireless access in these networks requires a comprehensive set of metrics and network monitoring data. Current solutions gather large amounts of data and require significant bandwidth and processing to offload and analyze this management traffic. As a result, these solutions are typically not scalable or real-time. To this end, we propose a multi-tiered approach to wireless network monitoring that dynamically controls the granularity of data collection based on observed events in the network. Our approach can achieve significant bandwidth savings and enable real-time automated management of a wireless network. Our initial analysis using traces from a large WLAN shows a significant reduction in the amount of data collected to diagnose problems in a WLAN.

Measurement-driven admission control on wireless backhaul networks

IEEE 802.11 wireless networks perform poorly in the presence of large traffic volumes. Measurements have shown that packet collisions and interference can lead to degraded performance to the extent that users experience unacceptably low throughput, which can ultimately lead to complete network breakdown [12]. An admission control framework that limits network flows can prevent network breakdown and improve the performance of throughput and delay-sensitive multimedia applications. In this paper, we present a measurement-driven admission control scheme that leverages wireless characteristics for intelligent flow control in a static wireless network. Experiments on the 25 node UCSB MeshNet show that the proposed admission control scheme can enhance network performance such that the QoS requirements of real time applications, such as VoIP, can be met.

MeshMon: a multi-tiered framework for wireless mesh networkmonitoring

Monitoring and troubleshooting a large wireless mesh network 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 is 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.

MeshMon: a multi-tiered framework for wireless mesh network monitoring

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