Research Items (33)
- Apr 2014
In this article, we introduce a power-hopping technique (PH-MAC) that, by alternating between different transmission power levels, aims to deliberately cause packet capture and thereby reduce the impact of collisions in 802.11 WLANs. We first devise an analytical model of the 802.11 protocol with heterogeneous capture probabilities, and show that, depending on the network load, the capture effect can enhance the throughput performance of all nodes. We base the design of PH-MAC on the findings following from this analysis and demonstrate that important performance improvements can be achieved by exploiting the interactions between the MAC and PHY layers to mitigate collisions. Finally, to understand the feasibility of this technique in practical deployments, we present a prototype implementation of PH-MAC which relies on commodity hardware and open-source drivers. We evaluate the performance of this implementation in an indoor testbed under different network conditions in terms of link qualities, network loads and traffic types. The experimental results obtained show that our scheme can provide significant gains over the default 802.11 mechanism in terms of throughput, fairness and delay.
Wireless networks increasingly utilize diverse spectral bands that exhibit vast differences in both transmission range and usage. In this work, we present MAWS (Mobile Access of Wide-Spectrum Networks), the first scheme designed for mobile clients to evaluate and select both APs and spectral bands in wide-spectrum networks. Because of the potentially vast number of spectrum and AP options, scanning may be prohibitive. Consequently, our key technique is for clients to infer channel quality and spectral usage for their current location and bands using limited measurements collected in other bands and at other locations. We experimentally evaluate MAWS via a wide-spectrum network that we deploy, a testbed providing access to four bands at 700 MHz, 900 MHz, 2.4 GHz and 5 GHz. To the best of our knowledge, the spectrum of these bands is the widest to be spanned to date by a single operational access network. A key finding of our evaluation is that under a diverse set of operating conditions, mobile clients can accurately predict their performance without a direct measurement at their current location and spectral bands.
The optimal configuration of the contention parameters of a WLAN depends on the network conditions in terms of number of stations and the traffic they generate. Following this observation, a considerable effort in the literature has been devoted to the design of distributed algorithms that optimally configure the WLAN parameters based on current conditions. In this paper, we propose a novel algorithm that, in contrast to previous proposals which are mostly based on heuristics, is sustained by mathematical foundations from multivariable control theory. A key advantage of the algorithm over existing approaches is that it is compliant with the 802.11 standard and can be implemented with current wireless cards without introducing any changes into the hardware or firmware. We study the performance of our proposal by means of theoretical analysis, simulations, and a real implementation. Results show that the algorithm substantially outperforms previous approaches in terms of throughput and delay.
- Mar 2018
Fitbit fitness trackers record sensitive personal information, including daily step counts, heart rate profiles, and locations visited. By design, these devices gather and upload activity data to a cloud service, which provides aggregate statistics to mobile app users. The same principles govern numerous other Internet-of-Things (IoT) services that target different applications. As a market leader, Fitbit has developed perhaps the most secure wearables architecture that guards communication with end-to-end encryption. In this article, we analyze the complete Fitbit ecosystem and, despite the brand's continuous efforts to harden its products, we demonstrate a series of vulnerabilities with potentially severe implications to user privacy and device security. We employ a range of techniques, such as protocol analysis, software decompiling, and both static and dynamic embedded code analysis, to reverse engineer previously undocumented communication semantics, the official smartphone app, and the tracker firmware. Through this interplay and in-depth analysis, we reveal how attackers can exploit the Fitbit protocol to extract private information from victims without leaving a trace, and wirelessly flash malware without user consent. We demonstrate that users can tamper with both the app and firmware to selfishly manipulate records or circumvent Fitbit's walled garden business model, making the case for an independent, user-controlled, and more secure ecosystem. Finally, based on the insights gained, we make specific design recommendations that can not only mitigate the identified vulnerabilities, but are also broadly applicable to securing future wearable system architectures.
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.
- Feb 2018
Future mobile networks will exploit unlicensed spectrum to boost capacity and meet growing user demands cost-effectively. The 3GPP has recently defined a Licensed-Assisted Access (LAA) scheme to enable global Unlicensed LTE (U-LTE) deployment, aiming at ($i$) ensuring fair coexistence with incumbent WiFi networks, i.e., impacting on their performance no more than another WiFi device, and ($ii$) achieving superior airtime efficiency as compared to WiFi. In this paper we show the standardized LAA fails to simultaneously fulfill these objectives, and design an alternative orthogonal (collision-free) listen-before-talk coexistence paradigm that provides a substantial improvement in performance, yet imposes no penalty on existing WiFi networks. We derive two LAA optimal transmission policies, ORLA and OLAA, that maximize LAA throughput in both asynchronous and synchronous (i.e., with alignment to licensed anchor frame boundaries) modes of operation, respectively. We present a comprehensive performance evaluation through which we demonstrate that, when aggregating packets, IEEE 802.11ac WiFi can be more efficient than 3GPP LAA, whereas our proposals can attain 100% higher throughput, without harming WiFi. We further show that long U-LTE frames incur up to 92% throughput losses on WiFi when using 3GPP LAA, whilst ORLA/OLAA sustain $>$200% gains at no cost, even in the presence of non-saturated WiFi and/or in multi-rate scenarios.
Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution technique that overcomes these problems by inferring narrowly localised traffic consumption from coarse measurements. We draw inspiration from image processing and design a deep-learning architecture tailored to mobile networking, which combines Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models. This enables to uniquely capture spatio-temporal relations between traffic volume snapshots routinely monitored over broad coverage areas (`low-resolution') and the corresponding consumption at 0.05 km $^2$ level (`high-resolution') usually obtained after intensive computation. Experiments we conduct with a real-world data set demonstrate that the proposed ZipNet(-GAN) infers traffic consumption with remarkable accuracy and up to 100$\times$ higher granularity as compared to standard probing, while outperforming existing data interpolation techniques. To our knowledge, this is the first time super-resolution concepts are applied to large-scale mobile traffic analysis and our solution is the first to infer fine-grained urban traffic patterns from coarse aggregates.
- Oct 2017
- International Symposium on Research in Attacks, Intrusions, and Defenses
Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors’ cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.
- Sep 2017
5G mobile networks are expected to provide pervasive high speed wireless connectivity, to support increasingly resource intensive user applications. Network hyper-densification therefore becomes necessary, though connecting to the Internet tens of thousands of base stations is non-trivial, especially in urban scenarios where optical fibre is difficult and costly to deploy. The millimetre wave (mm-wave) spectrum is a promising candidate for inexpensive multi-Gbps wireless backhauling, but exploiting this band for effective multi-hop data communications is challenging. In particular, resource allocation and scheduling of very narrow transmission/ reception beams requires to overcome terminal deafness and link blockage problems, while managing fairness issues that arise when flows encounter dissimilar competition and traverse different numbers of links with heterogeneous quality. In this paper, we propose WiHaul, an airtime allocation and scheduling mechanism that overcomes these challenges specific to multi-hop mm-wave networks, guarantees max-min fairness among traffic flows, and ensures the overall available backhaul resources are fully utilised. We evaluate the proposed WiHaul scheme over a broad range of practical network conditions, and demonstrate up to 5 times individual throughput gains and a fivefold improvement in terms of measurable fairness, over recent mm-wave scheduling solutions.
Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors' cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.
Millimetre-wave (mmWave) technology is a promising candidate for meeting the intensifying demand for ultra fast wireless connectivity, especially in high-end enterprise networks. Very narrow beam forming is mandatory to mitigate the severe attenuation specific to the extremely high frequency (EHF) bands exploited. Simultaneously, this greatly reduces interference, but generates problematic communication blockages. As a consequence, client association control and scheduling in scenarios with densely deployed mmWave access points become particularly challenging, while policies designed for traditional wireless networks remain inappropriate. In this paper we formulate and solve these tasks as utility maximisation problems under different traffic regimes, for the first time in the mmWave context. We specify a set of low-complexity algorithms that capture distinctive terminal deafness and user demand constraints, while providing near-optimal client associations and airtime allocations, despite the problems' inherent NP-completeness. To evaluate our solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol, which we construct upon preliminary 60GHz channel measurements. Simulation results demonstrate that our schemes provide up to 60% higher throughput as compared to the commonly used signal strength based association policy for mmWave networks, and outperform recently proposed load-balancing oriented solutions, as we accommodate the demand of 33% more clients in both static and mobile scenarios.
- Dec 2016
In this paper we tackle the digital exclusion problem in developing and remote locations by proposing Imola, an inexpensive learning-driven access mechanism for multi-hop wireless networks that operate across TV white-spaces (TVWS). Stations running Imola only rely on passively acquired neighbourhood information to achieve scheduled-like operation in a decentralised way, without explicit synchronisation. Our design overcomes pathological circumstances such as hidden and exposed terminals that arise due to carrier sensing and are exceptionally problematic in low frequency bands. We present a prototype implementation of our proposal and conduct experiments in a real test bed, which confirms the practical feasibility of deploying our solution in mesh networks that build upon the IEEE 802.11af standard. Finally, the extensive system level simulations we perform demonstrate that Imola achieves up to 4× more throughput than the channel access protocol defined by the standard and reduces frame loss rate by up to 100%.
- Oct 2016
- the 3rd Workshop
The mobile networking community is pursuing densification of small cell deployments to address the capacity crisis inherent to the projected exponential increase in mobile data traffic. Connecting massive numbers of access points to the Internet using optical fibre is however both very complex and expensive. In this paper we tackle small cell backhauling wirelessly, building upon recent advances in millimetre-wave technology. We propose a resource allocation algorithm for aggregate data flows traversing such multi-hop backhauls, and specify WiHaul, a light-weight hierarchical scheduling protocol that enforces the computed airtime shares and coordinates multi-hop transmissions effectively. To achieve high throughput performance while ensuring low demand flows are satisfied, we adopt a max-min fair allocation strategy. Results we present show our solution attains max-min fairness through a non-trivial partitioning of the airtime budget available in cliques of sub-flows, which depends on flow demands, their paths, and the capacities of the links traversed.
- Oct 2016
- ACM WiNTECH
Mobile operators are seeking to increase network capacity by extending Long Term Evolution (LTE) cellular operation into unlicensed frequency bands. While these efforts may respond to the projected exponential growth in mobile data traffic, significant concerns exist about the harmonious coexistence of LTE with incumbent Wi-Fi deployments. In this paper we characterise experimentally the LTE and Wi-Fi behaviour when sharing the same spectrum while operating under a broad range of network conditions. Specifically, we deploy a test bed with commodity Wi-Fi hardware and low-cost software-defined radio equipment running an open-source LTE stack. We investigate the user-level performance attainable over these technologies when employing different settings, including LTE duty cycling patterns, Wi-Fi offered loads, transmit power levels, modulation and coding schemes, and packet sizes. We show that co-existence is feasible without modifications to the Wi-Fi stack, if LTE periodically employs "silent" sub-frames; however, optimising the performance of both requires non-trivial tuning of multiple parameters in conjunction with close monitoring of Wi-Fi operation and detection of application-specific requirements. Our findings lay the foundations for coherent design of practical LTE/Wi-Fi co-existence mechanisms.
- Nov 2014
Recent experimental studies confirm the prevalence of the widely known performance anomaly problem and the severe network utility degradation this inflicts on current Wi-Fi networks. Although a large body of work addressed this issue, we attribute refusal of prior solutions to their poor implementation feasibility with off-the-shelf hardware and their imprecise modelling of the 802.11 protocol. Their applicability is further challenged today by very high throughput enhancements (802.11n/ac) whereby link speeds can vary by two orders of magnitude. Unlike earlier approaches, in this paper we introduce the first rigorous analytical model of 802.11 stations' throughput and airtime in multi-rate settings, without sacrificing accuracy for tractability. We use the proportional-fair allocation criterion to formulate network utility maximisation as a convex optimisation problem for which we give a closed-form solution. We present a fully functional light-weight implementation of our scheme on commodity access points and evaluate this extensively via experiments in a real deployment over a broad range of network conditions. Results demonstrate our proposal achieves up to 100\% utility gains, can double video streaming goodput and reduces TCP download times by 8x.
With the increasing availability of flexible wireless 802.11 devices, the potential exists for users to selfishly manipulate their channel access parameters and gain a performance advantage. Such practices can have a severe negative impact on compliant stations. To enable access points to counteract these selfish behaviours and preserve fairness in wireless networks, in this paper we propose a policing mechanism that drives misbehaving users into compliant operation without requiring any cooperation from clients. This approach is demonstrably effective against a broad class of misbehaviours, soundly-based, i.e. provably hard to circumvent and amenable to practical implementation on existing commodity hardware.
Question - Queuing model for wireless router - any suggestions?
A good start may be looking at the TWC '07 paper of Foh et al, where a detailed analysis of the 802.11 service time is given for different types of arrival processes and packet sizes.
As open-source WiFi device drivers are increasingly available, wireless equipment can be configured to disobey the 802.11 specification, with the goal of achieving performance gains, to the detriment of fair users.We demonstrate a practical implementation of a node policing scheme that combats such selfish behaviour, using commercial off-the-shelf hardware and a modified firmware. With a small testbed, we show that access points running our scheme can detect misbehaving stations, inflict punishment upon them and effectively restore fairness in the network.
While there have been considerable advances in the modelling of 802.11's MAC layer in recent years, 802.11 with finite buffer space is considered difficult to analyse. In this paper, we study the impact of finite buffers' effect on the 802.11 performance, in view of the requirements of interactive applications sensitive to delay and packet loss. Using both state-of-the art and simplified queueing models, we identify a surprising result. Specifically, we find that increased buffering throughout an 802.11 network will not only incur delay, but may actually increase the packet loss experienced by stations. By means of numerical analysis and simulations we show that this non-monotonic behaviour arises because of the contention-based nature of the medium access protocol, whose performance is closely related to the traffic load and the buffer size. Finally, we discuss on protocol and buffer tuning towards eliminating such undesirable effect.
- Aug 2012
In recent years, concerns on energy consumption and greenhouse pollution due to the operation of wireless devices have triggered a vast amount of research work on the so called green wireless technologies, leading to new, energy-aware proposals. Even for the case of battery powered devices, where energy conservation is a key design goal, new approaches have been proposed, based on a better understanding of the cost-performance trade-offs introduced by energy efficient operation, while increasingly focusing on emerging communication technologies, e.g., body sensor networks, MIMO or LTE. This paper presents a survey of the recent proposals for green wireless communications, with a view to understanding the most relevant sources of inefficient energy consumption and how these are tackled by current solutions. We introduce a classification of the existing mechanisms based on their operational time-scale, discuss the most important techniques employed to date from this perspective, analyze the employed evaluation methodologies and undertake a quantitative comparison of their performance gains. Following this analysis, we identify the key challenges yet to be addressed by the research community, as well as several possible future directions towards greener communications.
With the increasing demand for mobile Internet access, WLAN virtualization is becoming a promising solution for sharing wireless infrastructure among multiple service providers. Unfortunately, few mechanisms have been devised to tackle this problem and the existing approaches fail in optimizing the limited bandwidth and providing virtual networks with fairness guarantees. In this paper, we propose a novel algorithm based on control theory to configure the virtual WLANs with the goal of ensuring fairness in the resource distribution, while maximizing the total throughput. Our algorithm works by adapting the contention window configuration of each virtual WLAN to the channel activity in order to ensure optimal operation. We conduct a control-theoretic analysis of our system to appropriately design the parameters of the controller and prove system stability, and undertake an extensive simulation study to show that our proposal optimizes performance under different types of traffic. The results show that the mechanism provides a fair resource distribution independent of the number of stations and their level of activity, and is able to react promptly to changes in the network conditions while ensuring stable operation.
The EDCA mechanism of the IEEE 802.11 standard has been designed to support, among others, video traffic. This mechanism relies on a number of parameters whose configuration is left open by the standard. Although there are some recommended values for these parameters, they are fixed independent of the WLAN conditions, which results in suboptimal performance. Following this observation, a number of approaches in the literature have been devised to set the EDCA parameters based on an estimation of the WLAN conditions. However, these previous approaches are based on heuristics and hence do not guarantee optimized performance. In this article we propose a novel algorithm to adjust the EDCA parameters to carry video traffic which, in contrast to previous approaches, is sustained on mathematical foundations that guarantee optimal performance. In particular, our approach builds upon (i) an analytical model of the WLAN performance under video traffic, used to derive the optimal point of operation of EDCA, and (ii) a control theoretic designed mechanism which drives the WLAN to this point of operation. Via extensive simulations, we show that the proposed approach performs optimally and substantially outperforms the standard recommended configuration as well as previous adaptive proposals.
In practical WLAN deployments, the capture effect has been shown to enhance the performance of stations residing close to the AP, while putting at disadvantage the distant nodes. In this paper, we introduce an analytical model to characterise the performance of 802.11 devices with heterogeneous capture probabilities and different network loads, and explore the interaction between the MAC operation and PHY capture. Unlike previous studies, we reveal that the throughput of stations experiencing low capture probabilities can also benefit from the capture effect when the stations retaining high capture probabilities are not saturated. Following these findings, we design a power-hopping scheme for 802.11 MAC that exploits the benefits of the capture effect to improve performance in dense deployments where nodes experience similar channel conditions. We investigate the potential gains of this mechanism by implementing a practical approximation using commercial off-the-shelf hardware and open-source drivers and, by conducting experiments in a real testbed, we show that our scheme can significantly outperform the standard 802.11 protocol in terms of throughput. Published in: World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2012 IEEE International Symposium on a Date of Conference: 25-28 June 2012 Page(s): 1 - 9 E-ISBN : 978-1-4673-1237-0 Print ISBN: 978-1-4673-1238-7 INSPEC Accession Number: 12933000 Conference Location : San Francisco, CA Digital Object Identifier : 10.1109/WoWMoM.2012.6263697
- Mar 2012
In 802.11 WLANs, adapting the contention parameters to network conditions results in substantial performance improvements. Even though the ability to change these parameters has been available in standard devices for years, so far no adaptive mechanism using this functionality has been validated in a realistic deployment. In this paper we report our experiences with implementing and evaluating two adaptive algorithms based on control theory, one centralized and one distributed, in a large-scale testbed consisting of 18 commercial off-the-shelf devices. We conduct extensive measurements, considering different network conditions in terms of number of active nodes, link qualities and traffic generated. We show that both algorithms significantly outperform the standard configuration in terms of total throughput. We also identify the limitations inherent in distributed schemes, and demonstrate that the centralized approach substantially improves performance under a large variety of scenarios, which confirms its suitability for real deployments.
Wireless networks are extensively deployed due to their low cost and configuration easiness. However, they are not adapted to the new services and applications that are increasingly demanded by users. Current implementation of the IEEE 802.11 specification is supported in hardware devices and software developments, but they do not provide the adaptability that would enhance user experience in next generation networks. In this paper, we present a new wireless framework, based on the one currently supported by the Linux stack: mac80211. This new framework, named mac80211++, has been tailored to improve MAC features in terms of: (i) modularity, by defining different 802.11 MAC services; (ii) flexibility, by enabling dynamic configurability of the 802.11 MAC; (iii) virtualization, by managing parallel independent 802.11 MACs accessing the same system resources.
The increasing popularity of multi-hop wireless mesh networks, augmented with specific mechanisms required to support carrier-grade services, makes them an attractive alternative to classical backhaul solutions for network operators. However, current mesh deployments are typically small-sized, and, under real-time service requirements, it is yet unclear if they could scale to realistic network sizes. In this paper we present a carrier-grade mesh network architecture and conduct a thorough study of its scalability. In particular, we identify the main bottleneck problems and evaluate by means of simulations and practical experiments the costs induced by the modules that provide self-configuration, resource handling, routing and mobility support capabilities. The obtained results confirm that the control overhead of the proposed modules is low enough to permit mesh deployments spanning a network size of 100 nodes. Furthermore, our architecture and all proposed modules support dividing the whole network into sub-domains, which allows operating even larger networks.
Providing carrier grade services to a large number of mobile users is becoming an important challenge for wireless network operators. One promising so-lution for offering cost-efficient alternatives compared to classical cellular approaches is the use of wireless mesh networks along with the use of heterogeneous radio tech-nologies. In this paper we propose a MAC abstraction layer to lessen the management burden of heterogeneous radio technologies. This abstraction layer is intended to hide the complexity and specifics of different wireless interfaces, this way supporting the use of a single set of routing and capacity handling mechanisms.
The MAC layer of the 802.11 standard, based on the CSMA/CA mechanism, specifies a set of parameters to control the aggressiveness of stations when trying to access the channel. However, these parameters are statically set independently of the conditions of the WLAN (e.g. the number of contending stations), leading to poor performance for most scenarios. To overcome this limitation previous work proposes to adapt the value of one of those parameters, namely the CW, based on an estimation of the conditions of the WLAN. However, these approaches suffer from two major drawbacks: i) they require extending the capabilities of standard devices or ii) are based on heuristics. In this paper we propose a control theoretic approach to adapt the CW to the conditions of the WLAN, based on an analytical model of its operation, that is fully compliant with the 802.11e standard. We use a Proportional Integrator controller in order to drive the WLAN to its optimal point of operation and perform a theoretic analysis to determine its configuration. We show by means of an exhaustive performance evaluation that our algorithm maximizes the total throughput of the WLAN and substantially outperforms previous standard-compliant proposals.
The paper presents the designing principles of a management infrastructure for monitoring the capabilities of different network interface cards (gigabit Ethernet, Endace), both for traffic generation and capturing at reception. This evaluation is useful to asses the goodness of captured traffic analysis and QoS performance measurements, using PC based platforms. The idea was to implement the communication of the management information between an administration console and a set of distributed SNMP-based software measurement agents for GNU/Linux platforms that enable to perform QoS measurement sessions
The enhanced distributed channel access (EDCA) mechanism of the IEEE 802.11e standard provides quality-of-service (QoS) support through service differentiation by using different medium-access-control (MAC) parameters for different stations. The configuration of these parameters, however, is still an open research challenge, as the standard provides only a set of fixed recommended values that do not take into account the current wireless local area network (WLAN) conditions and, therefore, lead to suboptimal performance. In this paper, we propose a novel algorithm for EDCA that, given the throughput and delay requirements of the stations that are present in the WLAN, computes the optimal configuration of the EDCA parameters. We first present a throughput and delay analysis that provides the mathematical foundation upon which our algorithm is based. This analysis is validated through simulations of different traffic sources (both data and real time) and EDCA configurations. We then propose a mechanism to derive the optimal configuration of the EDCA parameters, given a set of performance criteria for throughput and delay. We assess the effectiveness of the configuration provided by our algorithm by comparing it against 1) the recommended values by the standard, 2) the results from an exhaustive search over the parameter space, and 3) previous configuration proposals, which are both standard and nonstandard compliant. Results show that our configuration outperforms all other approaches. European Community's Seventh Framework Program