36 research outputs found
Can User-Level Probing Detect and Diagnose Common Home-WLAN Pathologies?
Common WLAN pathologies include low signal-to-noise ratio, congestion, hidden
terminals or interference from non-802.11 devices and phenomena. Prior work has
focused on the detection and diagnosis of such problems using layer-2
information from 802.11 devices and special-purpose access points and monitors,
which may not be generally available. Here, we investigate a userlevel
approach: is it possible to detect and diagnose 802.11 pathologies with
strictly user-level active probing, without any cooperation from, and without
any visibility in, layer-2 devices? In this paper, we present preliminary but
promising results indicating that such diagnostics are feasible
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Modeling Environmental Effects on Directionality in Wireless Networks ; CU-CS-1044-08
Interference charecterisation, location and bandwidth estimation in emerging WiFi networks
Wireless LAN technology based on the IEEE 802.11 standard, commonly referred
to as WiFi, has been hugely successful not only for the last hop access to the Internet
in home, office and hotspot scenarios but also for realising wireless backhaul in mesh
networks and for point -to -point long- distance wireless communication. This success
can be mainly attributed to two reasons: low cost of 802.11 hardware from reaching
economies of scale, and operation in the unlicensed bands of wireless spectrum.The popularity of WiFi, in particular for indoor wireless access at homes and offices,
has led to significant amount of research effort looking at the performance issues
arising from various factors, including interference, CSMA/CA based MAC protocol
used by 802.11 devices, the impact of link and physical layer overheads on application
performance, and spatio-temporal channel variations. These factors affect the performance
of applications and services that run over WiFi networks. In this thesis, we
experimentally investigate the effects of some of the above mentioned factors in the
context of emerging WiFi network scenarios such as multi- interface indoor mesh networks,
802.11n -based WiFi networks and WiFi networks with virtual access points
(VAPs). More specifically, this thesis comprises of four experimental characterisation
studies: (i) measure prevalence and severity of co- channel interference in urban WiFi
deployments; (ii) characterise interference in multi- interface indoor mesh networks;
(iii) study the effect of spatio-temporal channel variations, VAPs and multi -band operation
on WiFi fingerprinting based location estimation; and (iv) study the effects of
newly introduced features in 802.11n like frame aggregation (FA) on available bandwidth
estimation.With growing density of WiFi deployments especially in urban areas, co- channel
interference becomes a major factor that adversely affects network performance. To
characterise the nature of this phenomena at a city scale, we propose using a new measurement
methodology called mobile crowdsensing. The idea is to leverage commodity
smartphones and the natural mobility of people to characterise urban WiFi co- channel
interference. Specifically, we report measurement results obtained for Edinburgh, a
representative European city, on detecting the presence of deployed WiFi APs via the
mobile crowdsensing approach. These show that few channels in 2.4GHz are heavily
used and there is hardly any activity in the 5GHz band even though relatively it
has a greater number of available channels. Spatial analysis of spectrum usage reveals
that co- channel interference among nearby APs operating in the same channel
can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city -scale are similar
to those of WiFi deployments in public spaces of different indoor environments. We
validate our approach in comparison with wardriving, and also show that our findings
generally match with previous studies based on other measurement approaches. As
an application of the mobile crowdsensing based urban WiFi monitoring, we outline a
cloud based WiFi router configuration service for better interference management with
global awareness in urban areas.For mesh networks, the use of multiple radio interfaces is widely seen as a practical
way to achieve high end -to -end network performance and better utilisation of
available spectrum. However this gives rise to another type of interference (referred to
as coexistence interference) due to co- location of multiple radio interfaces. We show
that such interference can be so severe that it prevents concurrent successful operation
of collocated interfaces even when they use channels from widely different frequency
bands. We propose the use of antenna polarisation to mitigate such interference and
experimentally study its benefits in both multi -band and single -band configurations. In
particular, we show that using differently polarised antennas on a multi -radio platform
can be a helpful counteracting mechanism for alleviating receiver blocking and adjacent
channel interference phenomena that underlie multi -radio coexistence interference.
We also validate observations about adjacent channel interference from previous
studies via direct and microscopic observation of MAC behaviour.Location is an indispensable information for navigation and sensing applications.
The rapidly growing adoption of smartphones has resulted in a plethora of mobile
applications that rely on position information (e.g., shopping apps that use user position
information to recommend products to users and help them to find what they want
in the store). WiFi fingerprinting is a popular and well studied approach for indoor
location estimation that leverages the existing WiFi infrastructure and works based on
the difference in strengths of the received AP signals at different locations. However,
understanding the impact of WiFi network deployment aspects such as multi -band
APs and VAPs has not received much attention in the literature. We first examine the
impact of various aspects underlying a WiFi fingerprinting system. Specifically, we
investigate different definitions for fingerprinting and location estimation algorithms
across different indoor environments ranging from a multi- storey office building to
shopping centres of different sizes. Our results show that the fingerprint definition
is as important as the choice of location estimation algorithm and there is no single
combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz
and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with
WiFi fingerprinting. Our results demonstrate that lower co- channel interference in the
5GHz band yields more accurate location estimation. We show that the inclusion of
VAPs has a significant impact on the location accuracy of WiFi fingerprinting systems;
we analyse the potential reasons to explain the findings.End -to -end available bandwidth estimation (ABE) has a wide range of uses, from
adaptive application content delivery, transport-level transmission rate adaptation and
admission control to traffic engineering and peer node selection in peer -to- peer /overlay
networks [ 1, 2]. Given its importance, it has been received much research attention in
both wired data networks and legacy WiFi networks (based on 802.11 a/b /g standards),
resulting in different ABE techniques and tools proposed to optimise different criteria
and suit different scenarios. However, effects of new MAC/PHY layer enhancements
in new and next generation WiFi networks (based on 802.11n and 802.11ac
standards) have not been studied yet. We experimentally find that among different
new features like frame aggregation, channel bonding and MIMO modes (spacial division
multiplexing), frame aggregation has the most harmful effect as it has direct
effect on ABE by distorting the measurement probing traffic pattern commonly used
to estimate available bandwidth. Frame aggregation is also specified in both 802.11n
and 802.1 lac standards as a mandatory feature to be supported. We study the effect of
enabling frame aggregation, for the first time, on the performance of the ABE using an
indoor 802.11n wireless testbed. The analysis of results obtained using three tools -
representing two main Probe Rate Model (PRM) and Probe Gap Model (PGM) based
approaches for ABE - led us to come up with the two key principles of jumbo probes
and having longer measurement probe train sizes to counter the effects of aggregating
frames on the performance of ABE tools. Then, we develop a new tool, WBest+ that
is aware of the underlying frame aggregation by incorporating these principles. The
experimental evaluation of WBest+ shows more accurate ABE in the presence of frame
aggregation.Overall, the contributions of this thesis fall in three categories - experimental
characterisation, measurement techniques and mitigation/solution approaches for performance
problems in emerging WiFi network scenarios. The influence of various factors
mentioned above are all studied via experimental evaluation in a testbed or real - world setting. Specifically, co- existence interference characterisation and evaluation
of available bandwidth techniques are done using indoor testbeds, whereas characterisation of urban WiFi networks and WiFi fingerprinting based location estimation are
carried out in real environments. New measurement approaches are also introduced
to aid better experimental evaluation or proposed as new measurement tools. These
include mobile crowdsensing based WiFi monitoring; MAC/PHY layer monitoring of
co- existence interference; and WBest+ tool for available bandwidth estimation. Finally,
new mitigation approaches are proposed to address challenges and problems
identified throughout the characterisation studies. These include: a proposal for crowd - based interference management in large scale uncoordinated WiFi networks; exploiting
antenna polarisation diversity to remedy the effects of co- existence interference
in multi -interface platforms; taking advantage of VAPs and multi -band operation for
better location estimation; and introducing the jumbo frame concept and longer probe
train sizes to improve performance of ABE tools in next generation WiFi networks
Implementation and experimental evaluation of Cooperative Awareness Basic Service for V2X Communications
A key aspect of Vehicle-to-Everything (V2X) communication is the concept of cooperative awareness, wherein the periodic exchange of status information allows vehicles to become aware of their surroundings for increased traffic safety and efficiency. This project aimed to implement the Cooperative Awareness (CA) basic service through the development of a low-cost, open-source On-board Unit (OBU)/Roadside Unit (RSU) that periodically broadcasts Cooperative Awareness Messages (CAM) using the 5.9 GHz band. Its proper operation and interoperability were verified by testing it with a commercial V2X device. This project also aimed to evaluate the effectiveness of the CA basic service through the development of an IEEE 802.11p-based V2X system simulator. The simulations were executed with varying vehicle traffic load (by changing the vehicle speed and the number of lanes) and CAM transmit frequency. The performance was then assessed by analyzing the Packet Reception Ratio (PRR), position error and Neighborhood Awareness Ratio (NAR) metrics. The presence of more vehicles in the slow speed and high lane count scenarios caused higher packet losses due to increased interference and collision probability, leading to low PRR and NAR values. Despite losing more CAMs, the slow speed scenarios had lower position errors since the displacement of vehicles was small. When the CAM transmit frequency was increased, the PRR decreased due to packet collisions. However, the position error was kept low as it benefited from the more frequent CAM transmissions and local database updates. Increasing the transmit frequency also increased the NAR, at least until a certain frequency threshold, beyond which the NAR started to worsen due to the dominant effect of interference in high message traffic situations
Bounding the Practical Error of Path Loss Models
We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios where a priori predictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible
Practical and Context-Aware Resource Adaptation in Mobile Networks
With the proliferation of various portable devices such as smart phones, netbooks and tablets, it becomes more important to design and implement effective resource management schemes with (i) the increasing number of users in the network and (ii) the expectation of frequent and fast mobility of network users. In this dissertation, we conclude that the key to solve the problem in mobile networks is adaptive resource allocation, which requires the system to behave in an adaptive manner considering the dynamic network conditions and various context of mobile users. Specifically, we study the following critical resource allocation issues in this dissertation: (i) rate adaptation; (ii) station handoff; (iii) load balancing; and (iv) power saving, for each we have proposed an adaptive scheme, implemented it in the MadWifi device driver, and demonstrated its effectiveness via experiments
A software framework for alleviating the effects of MAC-aware jamming attacks in wireless access networks
The IEEE 802.11 protocol inherently provides the same long-term throughput to all the clients associated with a given access point (AP). In this paper, we first identify a clever, low-power jamming attack that can take advantage of this behavioral trait: the placement of a lowpower jammer in a way that it affects a single legitimate client can cause starvation to all the other clients. In other words, the total throughput provided by the corresponding AP is drastically degraded. To fight against this attack, we design FIJI, a cross-layer anti-jamming system that detects such intelligent jammers and mitigates their impact on network performance. FIJI looks for anomalies in the AP load distribution to efficiently perform jammer detection. It then makes decisions with regards to optimally shaping the traffic such that: (a) the clients that are not explicitly jammed are shielded from experiencing starvation and, (b) the jammed clients receive the maximum possible throughput under the given conditions. We implement FIJI in real hardware; we evaluate its efficacy through experiments on two wireless testbeds, under different traffic scenarios, network densities and jammer locations. We perform experiments both indoors and outdoors, and we consider both WLAN and mesh deployments. Our measurements suggest that FIJI detects such jammers in realtime and alleviates their impact by allocating the available bandwidth in a fair and efficient way. © Springer Science+Business Media
Flexible Spectrum Assignment for Local Wireless Networks
In this dissertation, we consider the problem of assigning spectrum to wireless local-area networks (WLANs). In line with recent IEEE 802.11 amendments and newer hardware capabilities, we consider situations where wireless nodes have the ability to adapt not only their channel center-frequency but also their channel width. This capability brings an important additional degree of freedom, which adds more granularity and potentially enables more efficient spectrum assignments. However, it also comes with new challenges; when consuming a varying amount of spectrum, the nodes should not only seek to reduce interference, but they should also seek to efficiently fill the available spectrum. Furthermore, the performances obtained in practice are especially difficult to predict when nodes employ variable bandwidths. We first propose an algorithm that acts in a decentralized way, with no communication among the neighboring access points (APs). Despite being decentralized, this algorithm is self-organizing and solves an explicit tradeoff between interference mitigation and efficient spectrum usage. In order for the APs to continuously adapt their spectrum to neighboring conditions while using only one network interface, this algorithm relies on a new kind of measurement, during which the APs monitor their surrounding networks for short durations. We implement this algorithm on a testbed and observe drastic performance gains compared to default spectrum assignments, or compared to efficient assignments using a fixed channel width. Next, we propose a procedure to explicitly predict the performance achievable in practice, when nodes operate with arbitrary spectrum configurations, traffic intensities, transmit powers, etc. This problem is notoriously difficult, as it requires capturing several complex interactions that take place at the MAC and PHY layers. Rather than trying to find an explicit model acting at this level of generality, we explore a different point in the design space. Using a limited number of real-world measurements, we use supervised machine-learning techniques to learn implicit performance models. We observe that these models largely outperform other measurement-based models based on SINR, and that they perform well, even when they are used to predict performance in contexts very different from the context prevailing during the initial set of measurements used for learning. We then build a second algorithm that uses the above-mentioned learned models to assign the spectrum. This algorithm is distributed and collaborative, meaning that neighboring APs have to exchange a limited amount of control traffic. It is also utility-optimal -- a feature enabled both by the presence of a model for predicting performance and the ability of APs to collaboratively take decisions. We implement this algorithm on a testbed, and we design a simple scheme that enables neighboring APs to discover themselves and to implement collaboration using their wired backbone network. We observe that it is possible to effectively gear the performance obtained in practice towards different objectives (in terms of efficiency and/or fairness), depending on the utility functions optimized by the nodes. Finally, we study the problem of scheduling packets both in time and frequency domains. Such ways of scheduling packets have been made possible by recent progress in system design, which make it possible to dynamically tune and negotiate the spectrum band [...