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174
The Changing Usage of a Mature Campus-wide Wireless Network
- In Proceedings of ACM MOBICOM
, 2004
"... Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As "Wi-Fi" technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks. ..."
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Cited by 322 (12 self)
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Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As "Wi-Fi" technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks.
Zigzag decoding: Combating hidden terminals in wireless networks
, 2008
"... This paper presents ZigZag, an 802.11 receiver design that combats hidden terminals. ZigZag’s core contribution is a new form of interference cancellation that exploits asynchrony across successive collisions. Specifically, 802.11 retransmissions, in the case of hidden terminals, cause successive co ..."
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Cited by 158 (10 self)
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This paper presents ZigZag, an 802.11 receiver design that combats hidden terminals. ZigZag’s core contribution is a new form of interference cancellation that exploits asynchrony across successive collisions. Specifically, 802.11 retransmissions, in the case of hidden terminals, cause successive collisions. These collisions have different interference-free stretches at their start, which ZigZag exploits to bootstrap its decoding. ZigZag makes no changes to the 802.11 MAC and introduces no overhead when there are no collisions. But, when senders collide, ZigZag attains the same throughput as if the colliding packets were a priori scheduled in separate time slots. We build a prototype of ZigZag in GNU Radio. In a testbed of 14 USRP nodes, ZigZag reduces the average packet loss rate at hidden terminals from 72.6% to about 0.7%.
Analyzing the mac-level behavior of wireless networks in the wild.
- In ACM SIGCOMM Computer Communication Review
, 2006
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A general model of wireless interference
- ACM INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING
, 2007
"... We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propag ..."
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Cited by 102 (4 self)
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We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed measurements are easy to gather, requiring only O(N) measurements in an N-node networks. Compared to existing measurement-based models, our model advances the state of the art in three important ways. First, it goes beyond pairwise interference and models interference among an arbitrary number of senders. Second, it goes beyond broadcast transmissions and models the more common case of unicast transmissions. Third, it goes beyond homogeneous nodes and models the general case of heterogeneous nodes with different traffic demands and different radio characteristics. Using simulations and measurements from two different wireless testbeds, we show that the predictions of our model are accurate in a wide range of scenarios.
Automating cross-layer diagnosis of enterprise wireless networks
- In Proceedings of the ACM SIGCOMM Conference, Kyoto
, 2007
"... Modern enterprise networks are of sufficient complexity that even simple faults can be difficult to diagnose — let alone transient outages or service degradations. Nowhere is this problem more apparent than in the 802.11-based wireless access networks now ubiquitous in the enterprise. In addition to ..."
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Cited by 64 (8 self)
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Modern enterprise networks are of sufficient complexity that even simple faults can be difficult to diagnose — let alone transient outages or service degradations. Nowhere is this problem more apparent than in the 802.11-based wireless access networks now ubiquitous in the enterprise. In addition to the myriad complexities of the wired network, wireless networks face the additional challenges of shared spectrum, user mobility and authentication management. Not surprisingly, few organizations have the expertise, data or tools to decompose the underlying problems and interactions responsible for transient outages or performance degradations. In this paper, we present a set of analysis techniques and models to precisely determine all sources of data transfer delay due to media access and mobility in 802.11 networks — from the physical layer to the transport layer — as well as the interactions among them. While some sources of delay can be directly measured, many of the delay components, such as AP queuing, backoffs, contention, etc., must be inferred. To infer these delays from measurements, we develop a detailed model of MAC protocol behavior, both as it is described in the 802.11 specification as well as how it is implemented in vendor hardware. Combined with comprehensive traces of wireless activity taken from an enterprise network, we produce a complete delay breakdown for packet transmissions and pinpoint problems that constrain connectivity or limit performance. 1.
Diagnosing Wireless Packet Losses in 802.11: Separating Collision from Weak Signal
"... Abstract—It is well known that a packet loss in 802.11 can happen either due to collision or an insufficiently strong signal. However, discerning the exact cause of a packet loss, once it occurs, is known to be quite difficult. In this paper we take a fresh look at this problem of wireless packet lo ..."
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Cited by 58 (1 self)
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Abstract—It is well known that a packet loss in 802.11 can happen either due to collision or an insufficiently strong signal. However, discerning the exact cause of a packet loss, once it occurs, is known to be quite difficult. In this paper we take a fresh look at this problem of wireless packet loss diagnosis for 802.11-based communication and propose a promising technique called COLLIE. COLLIE performs loss diagnosis by using newly designed metrics that examine error patterns within a physical-layer symbol in order to expose statistical differences between collision and weak signal based losses. We implement COLLIE through custom driver-level modifications in Linux and evaluate its performance experimentally. Our results demonstrate that it has an accuracy ranging between 60-95 % while allowing a false positive rate of upto 2%. We also demonstrate the use of COLLIE in subsequent link adaptations in both static and mobile wireless usage scenarios through measurements on regular laptops and the Netgear SPH101 Voice-over-WiFi phone. In these experiments, COLLIE led to throughput improvements of 20-60% and reduced retransmission related costs by 40 % depending upon the channel conditions. I.
Understanding wifi-based connectivity from moving vehicles
- In IMC ’07: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
, 2007
"... Abstract – Using measurements from VanLAN, a modest-size testbed that we have deployed, we analyze the fundamental characteristics of WiFi-based connectivity between basestations and vehicles in urban settings. Our results uncover a more complex picture than previous work which was conducted in more ..."
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Cited by 41 (3 self)
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Abstract – Using measurements from VanLAN, a modest-size testbed that we have deployed, we analyze the fundamental characteristics of WiFi-based connectivity between basestations and vehicles in urban settings. Our results uncover a more complex picture than previous work which was conducted in more benign settings. The interval between a vehicle coming into and going out of range of a basestation is often marred by intermittent periods of very poor connectivity. These “gray periods ” are hard to reliably predict because their arrival is not signaled by metrics such as signal strength, loss rate, speed or distance from the basestation. At the same time, they also do not consistently occur at the same spot. Our analysis suggests that gray periods are not caused by the motion of the vehicle per se but by the variability in the urban radio environment combined with the vehicle traversing locations that are poorly covered by the basestation. We also find that knowledge of past connectivity can be used to identify regions where gray periods are more likely to occur as well as regions where the vehicle is likely to experience good connectivity.
SensorFlock: an Airborne Wireless Sensor Network of Micro-air Vehicles
- In Proc. ACM 5th Int’l Conf. on Embedded Networked Sensor Systems (SENSYS ’07), pp 117–129
, 2007
"... An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique threedimensional vantage for monitoring wildlife and ecological systems. This paper de ..."
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Cited by 41 (0 self)
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An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique threedimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in airto-air communication as well as air-to-ground communication.
LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics
, 2007
"... Understanding the behavior of deployed sensor networks is difficult as they become more sophisticated and larger in scale. Much of the difficulty comes from the lack of tools to provide a global view on the network dynamics. This paper describes LiveNet, a set of tools and techniques for reconstruct ..."
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Cited by 40 (3 self)
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Understanding the behavior of deployed sensor networks is difficult as they become more sophisticated and larger in scale. Much of the difficulty comes from the lack of tools to provide a global view on the network dynamics. This paper describes LiveNet, a set of tools and techniques for reconstructing complex dynamics of live sensor network deployments. LiveNet is based on the use of passive sniffers co-deployed with the network. We address several challenges: merging multiple sniffer traces, determining coverage of sniffers, inference of missing information for path reconstruction and high-level analyses with application-specific knowledge. To validate LiveNet’s accuracy, we conduct controlled experiments on an indoor testbed. Finally, we present data from a real deployment using LiveNet. The results show that LiveNet is able to to reconstruct network topology, bandwidth usage, routing paths, identify hot-spot nodes, and disambiguate failures observed at application level without instrumenting application code.
Analysis of a Mixed-Use Urban WiFi Network: When Metropolitan becomes Neapolitan
- IMC'08
, 2008
"... While WiFi was initially designed as a local-area access network, mesh networking technologies have led to increasingly expansive deployments of WiFi networks. In urban environments, the WiFi mesh frequently supplements a number of existing access technologies, including wired broadband networks, 3G ..."
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Cited by 39 (1 self)
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While WiFi was initially designed as a local-area access network, mesh networking technologies have led to increasingly expansive deployments of WiFi networks. In urban environments, the WiFi mesh frequently supplements a number of existing access technologies, including wired broadband networks, 3G cellular, and commercial WiFi hotspots. It is an open question what role city-wide WiFi deployments play in the increasingly diverse access network spectrum. We study the usage of the Google WiFi network deployed in Mountain View, California, and find that usage naturally falls into three classes, based almost entirely on client device type. Moreover, each of these classes of use has significant geographic locality, following the distribution of residential, commercial, and transportation areas of the city. Finally, we find a diverse set of mobility patterns that map well to the archetypal use cases for traditional access technologies.
