Stability study of the TCP-RED system using detrended fluctuation analysis

Author name used in this publication: Chi K. TseRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Exploiting phasor measurement units for enhanced transmission network operation and control

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel UniversityIn order to achieve binding Government targets towards the decarbonisation of the electricity network, the GB power system is undergoing an unprecedented amount of change. A series of new technologies designed to integrate massive volumes of renewable generation, predominantly in the form of offshore wind, asynchronously connecting to the periphery of the transmission system, are transforming the requirements of the network. This displacement of traditional thermal generation is leading to a significant reduction in system inertia, thus making the task of system operation more challenging. It is therefore deemed necessary to develop tools and technologies that provide far greater insight into the state of the power system in real-time and give rise to methods for improving offline modelling practices through an enhanced understanding of the systems performance. To that extent PMUs are seen as one of the key enablers of the Smart Grid, providing accurate time-synchronised measurements on the state of the power system, allowing the true dynamics of the power system to be captured and analysed. This thesis provides an analysis of the existing PMU deployment on the GB transmission system with a view to the future system monitoring requirements. A critical evaluation and comparison is also provided on the suitability of a University based Low Voltage PMU network to further enhance the visibility of the GB system. In addition a novel event detection algorithm based on Detrended Fluctuation Analysis is developed and demonstrated, designed to determine the exact start time of a transmission event, as well as the suitability of such an event for additional transmission system analysis, namely inertia estimation. Finally, a reliable method for the estimation of total system inertia is proposed that includes an estimate of the contribution from residual sources, of which there is currently no visibility. The proposed method identifies the importance of regional inertia and its impact to the operation of the GB transmission system.Engineering and Physical Sciences Research Council (EPSRC) and National Grid

Characterising postural sway fluctuations in humans using linear and nonlinear methods

Introduction: Postural control is a prerequisite to many everyday and sporting activities which requires the interaction of multiple sensorimotor processes. As long as we have no balance disorders, the maintenance of an erect standing position is taken for granted with automatic running control processes. It is well known that with increasing age or disease balance problems occur which often cause fall-related injuries. To assess balance performance, posturography is widely applied in which body sway is traditionally viewed as a manifestation of random fluctuations. Thus, the amount of sway is solely used as an index of postural stability, that is, less sway is an indication of better control. But, traditional measures of variability fail to account for the temporal organisation of postural sway. The concept of nonlinear dynamics suggests that variability in the motor output is not random but structured. It provides the stimulus to reveal the functionality of postural sway. This thesis evaluates nonlinear analysis tools in addition to classic linear methods in terms of age-related modifications of postural control and under different standing conditions in order to broaden the existing knowledge of postural control processes. Methods: Static posturographic analyses were conducted which included the recording of centre of pressure (COP) time series by means of a force plate. Linear and nonlinear methods were used to quantify postural sway variability in order to evaluate both the amount and structure of sway. Classic time and frequency domain COP parameters were computed. In addition, wavelet transform (WT), multiscale entropy, detrended fluctuation analysis, and scaled windowed variance method were applied to COP signals in order to derive structural COP parameters. Two experiments were performed. 1) 16 young (26.1 ± 6.7 years), healthy subjects were asked to adopt a bipedal stance under single- and dual-task conditions. Three trials were conduced each with a different sampling duration: 30, 60, and 300 seconds [s]. 2) 26 young (28.15 ± 5.86 years) and 13 elderly (72 ± 7 years) subjects stood quietly for 60 s on five different surfaces which imposed different biomechanical constraints: level ground (LG), one foot on a step (ST), uphill (UH), downhill (DH), and slope (SL). Additional to COP recordings, limb load symmetry was assessed via foot pressure insoles. Results: We found a higher sensitivity of structural COP parameters to modulations of postural control and partly an improved evaluation of sway dynamics in longer COP recordings. WT revealed a reweighing of frequency bands in response to altered standing conditions. Scaling exponents and entropy values of COP signals were task-dependent. Higher entropy values were found under the dual-task and condition ST. The time scales affected under the altered standing positions differed between groups and sway directions. Mainly larger posturograms were found in the elderly. Age effects were especially revealed in position ST and concerning medial-lateral COP signals. Load asymmetry was stronger in elderly subjects for LG, UH, and DH positions. Discussion: Modifications of multiple time scales corresponds to an interplay of control subsystems to cope with the altered task demands. The affected time scales are age-dependent suggesting a change of control processes. Higher irregularity under the dual-task indicates a more complex motor output which is interpreted as less attentional investment into postural control. Larger complexity is evident for ST in contrast to LG position. ST obviously challenges lateral sway which is counteracted differently between groups. Load asymmetry suggests that especially elderly subjects adopt a step-initiation strategy. Conclusion: A continued application of nonlinear methods is necessary to broaden the understanding of postural control mechanisms and to identify classifiers for balance dysfunctions. Structural COP parameters provide a more comprehensive indication of postural control system properties between groups and task demands. COP recordings of at least 60 s are recommended to adequately quantify COP signal structure. The analysis of postural strategies in everyday activities increases the ecological validity of postural control studies and can provide valuable information regarding the development of effective rehabilitation programs.Die posturale Kontrolle ist eine Voraussetzung für viele Alltagsaktivitäten und sportliche Bewegungen. Man weiß heute, dass den Kontrollmechanismen eine komplexe Interaktion sensomotorischer Prozesse unterliegt (Horak and Mcpherson, 1996; Oie et al., 2002). Solange keine Gleichgewichtsdefizite vorliegen, nehmen wir es als selbstverständlich wahr aufrecht Stehen zu können, ohne uns der Komplexität posturaler Kontrollmechanismen bewusst zu sein. Studien haben gezeigt, dass es mit zunehmendem Alter zu Defiziten in der posturalen Kontrolle kommt (Pasquier et al., 2003; Woollacott, 1993). Oftmals ist ein erhöhtes Sturzrisiko die Folge, welches unter anderem mit Verletzungen, einer eingeschränkten Mobilitätsowie einer verminderten Lebensqualität einhergehen kann (Era et al., 1997; Frank and Patla, 2003). Seit vielen Jahren schon werden posturographische Untersuchungen durchgeführt mit dem Ziel, posturale Kontrollmechanismen abzuleiten undDysfunktionen im posturalen System zu diagnostizieren (Piirtola and Era, 2006). Jedoch sind die Mechanismen, die der posturalen Kontrolle unterliegen, bis heute nicht eindeutig verstanden. Neue Erkenntnisse konnten in den letzten Jahrenvor allem durch ein erweitertes Verständnis von Bewegungsvariabilität gewonnen werden (Stergiou and Decker, 2011; Lippens and Nagel, 2009). Traditionell werden posturale Analysen unter der Annahme durchgeführt und interpretiert, dass Variabilität eine Art “Rauschen” (white noise) ist und somit Ausdruck eines Fehlers. Posturale Schwankungen werden als zufällige, nicht intendierte Abweichungen gesehen (Loosch, 1997). Der Parameter “Schwankungsausmaß” wird zur Diagnostik des statischen Gleichgewichts herangezogen und bei einer größeren Schwankung wird eine schlechtere posturale Kontrolle diagnostiziert. Im Gegensatz dazu weist der systemdynamische Modellansatz auf die funktionale Rolle der Variabilität hin (van Emmerik and van Wegen, 2002). Variabilität ist Ausdruck der Anpassung und Flexibilität und somit notwendig, um auf ständige Umweltveränderungen reagieren zu können. Ein erhöhtes Schwankungsausmaß ist demnach nicht ausschließlich ein Zeichen für Instabilität (Newell et al., 1993). Eine größere Variabilität posturaler Schwankungen kann auch positiv im Sinne von mehr Umweltexploration interpretiert werden (Lacour et al., 2008). So konnte gezeigt werden, dass posturale Schwankungen nicht zufällig sind, sondern eine Struktur enthalten (Duarte and Zatsiorsky, 2000), dessen Charakterisierung zusätzliche Informationen über die Organisation des posturalen Kontrollsystems liefert (Stergiou and Decker, 2011). Die vorliegende Arbeit evaluiert nichtlineare Methoden unter dem systemdynamischen Ansatz zusätzlich zu den traditionell eingesetzten linearen Methoden. Ziel ist es, neben der Quantifizierung des Ausmaßes posturaler Schwankungen ihre Struktur zu charakterisieren, um das Verständnis für posturale Kontrollmechanismen zu erweitern. Die Evaluierung erfolgt zunächst über den Vergleich von Stehen mit und ohne kognitiver Zusatzaufgabe, wo Studien erste Hinweise auf eine veränderte COP1 Signalstruktur geben (Cavanaugh et al., 2007; Donker et al., 2007; Stins et al., 2009). Durch das Betrachten unterschiedlicher Signallängen und eines umfangreichen Methodenspektrums sollen Anhaltspunkte für die Applikation vonnichtlinearen in Kombination mit linearen Analyseverfahren abgeleitet werden. In einer zweiten Untersuchung werden diese dann in einem angewandten Studiendesign umgesetzt. Dabei wird die Veränderung posturaler Kontrollstrategien bei unterschiedlichen Standpositionen untersucht, welche alltägliche Situationen simulieren, unter Berücksichtigung altersbedingter Effekte. Dies ist ein erster Ansatz zur Erreichung einer hohen ökologischen Validität posturaler Studien (Frank and Patla, 2003; Visser et al., 2008). Erst kürzlich wurde gezeigt, dass bei älteren Menschen meist interne Auslöser (z.B. Gewichtsverlagerungen) ursächlich für Stürze sind (Robinovitch et al., 2013). Zudem haben ältere Personen größere Schwierigkeiten auf Umgebungsveränderungen zu reagieren (Nardone and Schieppati, 2010). Es ist jedoch bisher unbekannt, wie sich Defizite in der Gleichgewichtskontrolle älterer Menschen auf die Struktur posturaler Schwankungen auswirken. ..

Wide-Area Synchrophasor Data Server System and Data Analytics Platform

As synchrophasor data start to play a significant role in power system operation and dynamic study, data processing and data analysis capability are critical to Wide-area measurement systems (WAMS). The Frequency Monitoring Network (FNET/GridEye) is a WAMS network that collects data from hundreds of Frequency Disturbance Recorders (FDRs) at the distribution level. The previous FNET/GridEye data center is limited by its data storage capability and computation power. Targeting scalability, extensibility, concurrency and robustness, a distributed data analytics platform is proposed to process large volume, high velocity dataset. A variety of real-time and non-real-time synchrophasor data analytics applications are hosted by this platform. The computation load is shared with balance by multiple nodes of the analytics cluster, and big data analytics tools such as Apache Spark are adopted to manage large volume data and to boost the data processing speed. Multiple power system disturbance detection and analysis applications are redesigned to take advantage of this platform. Data quality and data security are monitored in real-time. Future data analytics applications can be easily developed and plugged into the system with simple configuration

Modelling computer network traffic using wavelets and time series analysis

Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fashion. Thus in this dissertation the activity of building time series models for IoT network traffic is undertaken. In the work that follows a broad review of the historical development of network traffic modelling is presented tracing a path that leads to the use of time series analysis for the said task. An introduction to time series analysis is provided in order to facilitate the theoretical discussion regarding the feasibility and suitability of time series analysis techniques for modelling network traffic. The theory is then followed by a summary of the techniques and methodology that might be followed to detect, remove and/or model the typical characteristics associated with network traffic such as linear trends, cyclic trends, periodicity, fractality, and long range dependence. A set of experiments is conducted in order determine the effect of fractality on the estimation of AR and MA components of a time series model. A comparison of various Hurst estimation techniques is also performed on synthetically generated data. The wavelet-based Abry-Veitch Hurst estimator is found to perform consistly well with respect to its competitors, and the subsequent removal of fractality via fractional differencing is found to provide a substantial improvement on the estimation of time series model parameters

Approaches and Techniques for Fingerprinting and Attributing Probing Activities by Observing Network Telescopes

The explosive growth, complexity, adoption and dynamism of cyberspace over the last decade has radically altered the globe. A plethora of nations have been at the very forefront of this change, fully embracing the opportunities provided by the advancements in science and technology in order to fortify the economy and to increase the productivity of everyday's life. However, the significant dependence on cyberspace has indeed brought new risks that often compromise, exploit and damage invaluable data and systems. Thus, the capability to proactively infer malicious activities is of paramount importance. In this context, generating cyber threat intelligence related to probing or scanning activities render an effective tactic to achieve the latter. In this thesis, we investigate such malicious activities, which are typically the precursors of various amplified, debilitating and disrupting cyber attacks. To achieve this task, we analyze real Internet-scale traffic targeting network telescopes or darknets, which are defined by routable, allocated yet unused Internet Protocol addresses. First, we present a comprehensive survey of the entire probing topic. Specifically, we categorize this topic by elaborating on the nature, strategies and approaches of such probing activities. Additionally, we provide the reader with a classification and an exhaustive review of various techniques that could be employed in such malicious activities. Finally, we depict a taxonomy of the current literature by focusing on distributed probing detection methods. Second, we focus on the problem of fingerprinting probing activities. To this end, we design, develop and validate approaches that can identify such activities targeting enterprise networks as well as those targeting the Internet-space. On one hand, the corporate probing detection approach uniquely exploits the information that could be leaked to the scanner, inferred from the internal network topology, to perform the detection. On the other hand, the more darknet tailored probing fingerprinting approach adopts a statistical approach to not only detect the probing activities but also identify the exact technique that was employed in the such activities. Third, for attribution purposes, we propose a correlation approach that fuses probing activities with malware samples. The approach aims at detecting whether Internet-scale machines are infected or not as well as pinpointing the exact malware type/family, if the machines were found to be compromised. To achieve the intended goals, the proposed approach initially devises a probabilistic model to filter out darknet misconfiguration traffic. Consequently, probing activities are correlated with malware samples by leveraging fuzzy hashing and entropy based techniques. To this end, we also investigate and report a rare Internet-scale probing event by proposing a multifaceted approach that correlates darknet, malware and passive dns traffic. Fourth, we focus on the problem of identifying and attributing large-scale probing campaigns, which render a new era of probing events. These are distinguished from previous probing incidents as (1) the population of the participating bots is several orders of magnitude larger, (2) the target scope is generally the entire Internet Protocol (IP) address space, and (3) the bots adopt well-orchestrated, often botmaster coordinated, stealth scan strategies that maximize targets' coverage while minimizing redundancy and overlap. To this end, we propose and validate three approaches. On one hand, two of the approaches rely on a set of behavioral analytics that aim at scrutinizing the generated traffic by the probing sources. Subsequently, they employ data mining and graph theoretic techniques to systematically cluster the probing sources into well-defined campaigns possessing similar behavioral similarity. The third approach, on the other hand, exploit time series interpolation and prediction to pinpoint orchestrated probing campaigns and to filter out non-coordinated probing flows. We conclude this thesis by highlighting some research gaps that pave the way for future work

Feedback control of flow separation using synthetic jets

The primary goal of this research is to assess the effect of synthetic jets on flow separation and provide a feedback control strategy for flow separation using synthetic jets. The feedback control synthesis is conducted based upon CFD simulation for a rounded backward-facing step. The results of the synthetic jet experiments on an airfoil showed that synthetic jets have the potential for controlling the degree of flow separation beyond delaying the onset of flow separation. In the simulation, while the jet is ejected slightly upstream from the separation point, the feedback pressure signal is acquired at a downstream wall point where the vortex is fully developed. Due to the uniqueness of synthetic jets, i.e. "zero-net-mass flux", the profile of synthetic jet velocity cannot be arbitrarily generated. The possible control variables are the magnitude or frequency of the oscillating jet velocity. Consequently, the fluidic system in simulation consists of the actuator model and the NARMAX (Nonlinear Auto Regressive Moving Average with eXogenous inputs) flow model. This system shows a strong nonlinear pressure response to the input jet frequency. Low-pass filtering of the pressure response, introduced for pressure recovery, facilitates a quasi-linear approximation of the system in the frequency domain using the describing function method. The low-pass filter effectively separates the pressure response into two frequency bands. The lower frequency band below the filter pass frequency includes the quasi-linear response targeted by the feedback control and the higher band above the filter stop frequency contains the attenuated higher harmonics, which are treated as nonlinear disturbances. This quasi-linear approximation is utilized to design a PI controller for the fluidic system including the synthetic jet. To ensure one-to-one correspondence of the jet frequency and the filtered pressure response, the upper bound of the jet frequency is set at the frequency of the maximum pressure. The response of the resulting closed loop feedback control system, comprised of a PI controller, low-pass filter, SJA model and NARMAX model, is shown to track the desired pressure command with an improvement in the transient response over the open-loop system

Novel performance evaluation of information and communication technologies to enable wide area monitoring systems for enhanced transmission network operation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The penetration of renewable energy sources has increased significantly in recent years due to the ongoing depletion of conventional resources and the transition to a low carbon energy system. Renewable energy sources such as wind energy are highly intermittent and unpredictable in nature, which makes the operation of the power grid more dynamic and therefore more complex. In order to operate the power system reliably under such conditions, Phasor Measurement Units (PMUs) through the use of satellite technology can offer a state-of-the-art Wide Area Monitoring System (WAMS) for improving power system monitoring, control and protection. They can improve the operation by providing highly precise and synchronised measurements near to real-time with higher frequency and accuracy. In order to achieve such objectives, a high-speed and reliable communications infrastructure is required to transfer time-critical PMU data from remote locations to the control centre. The signals measured by PMUs are transmitted across Local and Wide Area Networks, where they may encounter excessive delays. Signal delays can have a disruptive effect and make applications at best inefficient and at worse ineffective. The main research contribution of this thesis is the performance evaluation of communication infrastructures for WAMS. The evaluation begins from inside substations and continues over wide areas from substations to control centre. Through laboratory-based investigations and simulations, the performance of communications infrastructure in a typical power system substation has been analysed. In addition, the performance evaluation of WAMS communications infrastructure has been presented. In the modelling and analysis, an existing WAMS as installed on the GB transmission system has been considered. The actual PMU packets as received at the Phasor Data Concentrator (PDC) were captured for latency analysis. A novel algorithmic procedure has been developed and implemented to automate the large-scale latency calculations. Furthermore, the internal delays of PMUs have been investigated, determined and analysed. Subsequently, the WAMS has been simulated and detailed comparisons have been performed between the simulated model results and WAMS performance data captured from the actual WAMS. The validated WAMS model has been used for analysing possible future developments as well as to test newly proposed mechanisms, protocols, etc. in order to improve the communications infrastructure performance

Seismological data acquisition and signal processing using wavelets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed