IEEE 802.11 wireless LAN traffic analysis: a cross-layer approach

textThe deployment of broadband wireless data networks, e.g., wireless local area networks (WLANs) [29], experienced tremendous growth in the last several years, and this trend is continuously gaining momentum. In fact, WLAN is becoming an indispensable component of the modern telecommunication infrastructure. Despite this optimistic outlook, however, little is known about the impact of the wireless channel on the characteristics of WLAN traffic. This dissertation characterizes the correlation structures of WLAN channel with traffic statistics from a cross-layer point of view, and provides new measurement methodologies and statistical models for WLAN networks. Currently WLAN standards are designed within the paradigm of the layered network architecture. For example, the architecture of IEEE 802.11 vii is almost identical to the Ethernet. However, wireless networks are fundamentally different from their wired peers due to the shift of transmission media from cables to over-the-air radio waves. This transition exposes wireless systems to the influence of radio propagation, and more importantly, to the temporal and spacial fluctuations of the radio channel that can actually be propagated up to upper layers. However, the current WLAN architecture isolates network layers, and largely ignores this impact. Therefore, we believe that a cross-layer based approach is necessary to understand and reflect this underlying impact of the channel to the upper layers of the network, especially in relation to WLAN traffic behavior. Measurement is one of the fundamental tools used to quantify radio propagation. As part of this dissertation, a complete framework for a measurement methodology, including hardware, software, and measurement procedures, is established. Characteristics of the propagation channel are estimated from measurement data, and the channel knowledge is applied to the upper layers for more realistic and accurate modeling. In WLAN environments, knowledge of the traffic characteristics is essential for proper network provisioning, and for improving the performance of the IEEE 802.11 standard and network devices, e.g., to design improved MAC schemes, or to build better buffer scheduling algorithms with channel knowledge, etc. Built upon extensive WLAN traffic traces, this dissertation work presents cross-layer models for WLAN throughput predictions, traffic statistics, and link layer characteristics. viii The main goal of this dissertation work is to experiment with and develop new methods for identifying channel characteristics. Thereby utilizing this knowledge, we show how to predict and improve WLAN performance. Within the framework of the developed cross-layer measurement methodology, we conducted extensive measurements in different physical environments and different settings such as office buildings and stores, and (1) show that the impact of the propagation channel can be quantified by using simple large scale channel metric (throughput over longer period of time), and (2) also present the existence of a Doppler effect within today’s WLAN packet traffic at sub-second time scales. We also show the real-world WLAN usage pattern from our measurement results. From this data, we conclude that the key issues to study WLAN networks include accurate site-specific propagation channel modeling and real-time autonomous traffic control.Electrical and Computer Engineerin

Modeling the Impact of Protocols on Traffic Burstiness At Large Timescales in Wireless Multi-Hop Networks

We investigate the impact of the protocol stack on traffic burstiness at large time-scales in wireless multi-hop network traffic. Origins of traffic burstiness at large scales (like its LRD nature) have been mostly attributed to the heavy-tails in traffic sources. In wired networks, protocol dynamics have little impact on large time-scale dynamics. However, given the nature of wireless networks, the MAC and routing layers together can lead to route flapping or oscillations even in a static network. Hence, we explore whether these dynamics can lead to traffic burstiness and LRD. Using network simulations, we analyze traffic for two MANET routing protocols - OLSR and AODV. By varying the routing protocol parameters, we analyze their role in inducing or preventing route oscillations, and study their impact on traffic LRD. We find that, losses in OLSR control packets, due to congestion at the MAC, can lead to route oscillations and traffic burstiness at large timescales. By tuning the parameters, route oscillations and traffic LRD can be avoided. AODV dynamics show little evidence for traffic LRD, even though we cannot rule out this possibility. We also show that the route oscillations can have heavier body and tail than exponential distribution, and that the Markovian framework for route oscillations is inadequate to explain the observed traffic scaling. Lastly, we give a model that captures the MAC and OLSR routing protocol interactions and depending upon chosen protocol parameters and input load, correctly predicts the presence of traffic LRD. Thus, we use this model to design appropriate choice of protocol parameters to mitigate traffic burstiness at large-timescales.Research supported by the Army Research Office under MURI award W911NF-08-1-0238 and by the National Science Foundation under grant CNS1018346

Video traffic : characterization, modelling and transmission

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multiscaling analysis and modelling of bursty impulsive noise in broadband power line communication channels.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.Power line communication (PLC) networks have the potential to offer broadband application services to homes and small offices cheaply since no additional wiring is required for it implementation. However, like other communication systems, it has its own challenges and the understanding of its channel characteristics is key to its optimal performance evaluation and deployment. Multipath propagation due to impedance mismatch and bursty impulsive noise are the important challenges that must be understood and their effects minimized for optimal system performance. Noise in power line communication networks is non-Gaussian and as such cannot be modelled as the convenient additive white Gaussian noise. The noise is known to be impulsive and in most cases, occurs in bursts. Therefore, it can be referred as bursty impulsive noise. Due to unique nature of this noise in power line channels, modulation and decoding schemes optimized for Gaussian channels may not necessarily work well in PLC systems. Recently developed noise models though take into consideration memory inherent in PLC noise, models capturing both long range correlations and multiscaling behaviour are not yet available in the literature. Furthermore, even though it is known that PLC noise has memory (i.e., it is correlated), the statistical properties of it is not well documented in the literature and will be the focus of this thesis. In this thesis, multiscaling behaviour of PLC noise is investigated. Both fractal and multifractal analysis methods are employed on noise data collected in three different scenarios (small offices, stand-alone apartment and University electronic laboratory) and their characteristics analysed. Multifractal analysis is employed since it is able to characterize both the strengths and frequency of occurrence of bursts in power line noise. Specifically, the contributions in this thesis are as follows: Firstly, empirical evidence is provided that PLC noise clearly manifests long range correlations behaviour. This is achieved by calculating the Hurst parameter (which is a measure of self similarity) in data from the above scenarios. Various methods employed to estimate this Hurst parameter reveal that in all the scenarios, long range dependence is evidenced. Secondly, multifractal detrended fluctuation analysis (MDFA) and multifractal detrending moving average (MDMA) analysis have been used to investigate the temporal correlations and scaling behaviour of power line channel noise measured from the three different scenarios mentioned earlier. Empirical results show that power line noise clearly manifests both long-range correlation and multifractal scaling behaviour with different strengths depending on the environments where they were captured. From the estimated singularity spectrum which is left truncated, it is evident from the two methods used that power line noise is sensitive to small fluctuations and is characterized by large scaling exponents. Multifractal analysis of the reshuffled time series noise reveal that the multifractal nature of PLC noise is as a result of long range correlation inherent in the noise and not from the heavy tailed distributions in it. Thirdly, we propose a multiplicative cascade model for PLC noise that is able to reproduce the empirical findings concerning the PLC noise time series: its local scaling behaviour and long range correlations. Model parameters are derived from the shape of multifractal spectrum of the PLC time series noise collected from measurement campaigns. Since in the recent past, the main challenge in PLC systems has been on how to model bursty impulsive PLC noise, the proposed model will be very useful in evaluating system performance of PLC networks in the presence of the bursty impulsive noise inherent in PLC networks. Moreover, bursts of different frequencies and strengths can be modelled by this proposed model and hence their effects on system performance evaluated. This will also open up investigations into designing modulation and decoding schemes that are optimal in systems prone to bursty impulsive noise

Analysis and assessment software for multi-user collaborative cognitive radio networks

Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features

Cross-Layer QoE Improvement with Dynamic Spectrum Allocation in OFDM-Based Cognitive Radio.

PhDRapid development of devices and applications results in dramatic growth of wireless tra c, which leads to increasing demand on wire- less spectrum resources. Current spectrum resource allocation pol- icy causes low e ciency in licensed spectrum bands. Cognitive Ra- dio techniques are a promising solution to the problem of spectrum scarcity and low spectrum utilisation. Especially, OFDM based Cog- nitive Radio has received much research interest due to its exibility in enabling dynamic resource allocation. Extensive research has shown how to optimise Cognitive Radio networks in many ways, but there has been little consideration of the real-time packet level performance of the network. In such a situation, the Quality of Service metrics of the Secondary Network are di cult to guarantee due to uctuating resource availability; nevertheless QoS metric evaluation is actually a very important factor for the success of Cognitive Radio. Quality of Experience is also gaining interest due to its focus on the users' per- ceived quality, and this opens up a new perspective on evaluating and improving wireless networks performance. The main contributions of this thesis include: it focuses on the real-time packet level QoS (packet delay and loss) performance of Cognitive Radio networks, and eval- uates the e ects on QoS of several typical non-con gurable factors including secondary user service types, primary user activity patterns and user distance from base station. Furthermore, the evaluation results are uni ed and represented using QoE through existing map- ping techniques. Based on the QoE evaluation, a novel cross layer RA scheme is proposed to dynamically compensate user experience, and this is shown to signi cantly improve QoE in scenarios where traditional RA schemes fail to provide good user experience

Graph-theoretic channel modeling and topology control protocols for wireless sensor networks

This report addresses two different research problems: (i) It presents a wireless channel model that reduces the complexity associated with high order Markov chains; and (ii) presents energy efficient topology control protocols which provide reliability while maintaining the topology in an energy efficient manner. For the above problems, real wireless sensor network traces were collected and extensive simulations were performed for evaluating the proposed protocols. Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this report, a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget is presented. The implication of unused states in complexity reduction of higher order Markov model is also explained. The trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models. Furthermore, a methodology to preserve energy is presented to increase the network lifetime by reducing the node degree forming an active backbone while considering network connectivity. However, in energy stringent wireless sensor networks, it is of utmost importance to construct the reduced topology with the minimal control overhead. Moreover, most wireless links in practice are lossy links with connectivity probability which desires that a routing protocol provides routing flexibility and reliability at a minimum energy consumption cost. For this purpose, distributed and semi-distributed novel graph-theoretic topology construction protocols are presented that exploit cliques and polygons in a WSN to achieve energy efficiency and reliability. The proposed protocols also facilitate load rotation under topology maintenance, thereby extending the network lifetime. In addition to the above, the report also evaluates why the backbone construction using connected dominating set (CDS) in certain cases remains unable to provide connected sensing coverage in the area covered. For this purpose, a novel protocol that reduces the topology while considering sensing area coverage is presented