Bayesian precalibration of a large stochastic microsimulation model

Calibration of stochastic traffic microsimulation models is a challenging task. This paper proposes a fast iterative probabilistic precalibration framework and demonstrates how it can be successfully applied to a real-world traffic simulation model of a section of the M40 motorway and its surrounding area in the U.K. The efficiency of the method stems from the use of emulators of the stochastic microsimulator, which provides fast surrogates of the traffic model. The use of emulators minimizes the number of microsimulator runs required, and the emulators' probabilistic construction allows for the consideration of the extra uncertainty introduced by the approximation. It is shown that automatic precalibration of this real-world microsimulator, using turn-count observational data, is possible, considering all parameters at once, and that this precalibrated microsimulator improves on the fit to observations compared with the traditional expertly tuned microsimulation. © 2000-2011 IEEE.

Fluctuation-driven traffic congestion in a scale-free model of the Internet

In studies of complex heterogeneous networks, particularly of the Internet, significant attention was paid to analysing network failures caused by hardware faults or overload. There network reaction was modelled as rerouting of traffic away from failed or congested elements. Here we model network reaction to congestion on much shorter time scales when the input traffic rate through congested routes is reduced. As an example we consider the Internet where local mismatch between demand and capacity results in traffic losses. We describe the onset of congestion as a phase transition characterised by strong, albeit relatively short-lived, fluctuations of losses caused by noise in input traffic and exacerbated by the heterogeneous nature of the network manifested in a power-law load distribution. The fluctuations may result in the network strongly overreacting to the first signs of congestion by significantly reducing input traffic along the communication paths where congestion is utterly negligible. © 2013 IEEE.

Traffic based energy consumption optimisation to improve the lifetime and performance of ad hoc wireless sensor networks

Ad hoc wireless sensor networks (WSNs) are formed from self-organising configurations of distributed, energy constrained, autonomous sensor nodes. The service lifetime of such sensor nodes depends on the power supply and the energy consumption, which is typically dominated by the communication subsystem. One of the key challenges in unlocking the potential of such data gathering sensor networks is conserving energy so as to maximize their post deployment active lifetime. This thesis described the research carried on the continual development of the novel energy efficient Optimised grids algorithm that increases the WSNs lifetime and improves on the QoS parameters yielding higher throughput, lower latency and jitter for next generation of WSNs. Based on the range and traffic relationship the novel Optimised grids algorithm provides a robust traffic dependent energy efficient grid size that minimises the cluster head energy consumption in each grid and balances the energy use throughout the network. Efficient spatial reusability allows the novel Optimised grids algorithm improves on network QoS parameters. The most important advantage of this model is that it can be applied to all one and two dimensional traffic scenarios where the traffic load may fluctuate due to sensor activities. During traffic fluctuations the novel Optimised grids algorithm can be used to re-optimise the wireless sensor network to bring further benefits in energy reduction and improvement in QoS parameters. As the idle energy becomes dominant at lower traffic loads, the new Sleep Optimised grids model incorporates the sleep energy and idle energy duty cycles that can be implemented to achieve further network lifetime gains in all wireless sensor network models. Another key advantage of the novel Optimised grids algorithm is that it can be implemented with existing energy saving protocols like GAF, LEACH, SMAC and TMAC to further enhance the network lifetimes and improve on QoS parameters. The novel Optimised grids algorithm does not interfere with these protocols, but creates an overlay to optimise the grids sizes and hence transmission range of wireless sensor nodes.

Modelling data and voice traffic over IP networks using continuous-time Markov models

Common approaches to IP-traffic modelling have featured the use of stochastic models, based on the Markov property, which can be classified into black box and white box models based on the approach used for modelling traffic. White box models, are simple to understand, transparent and have a physical meaning attributed to each of the associated parameters. To exploit this key advantage, this thesis explores the use of simple classic continuous-time Markov models based on a white box approach, to model, not only the network traffic statistics but also the source behaviour with respect to the network and application. The thesis is divided into two parts: The first part focuses on the use of simple Markov and Semi-Markov traffic models, starting from the simplest two-state model moving upwards to n-state models with Poisson and non-Poisson statistics. The thesis then introduces the convenient to use, mathematically derived, Gaussian Markov models which are used to model the measured network IP traffic statistics. As one of the most significant contributions, the thesis establishes the significance of the second-order density statistics as it reveals that, in contrast to first-order density, they carry much more unique information on traffic sources and behaviour. The thesis then exploits the use of Gaussian Markov models to model these unique features and finally shows how the use of simple classic Markov models coupled with use of second-order density statistics provides an excellent tool for capturing maximum traffic detail, which in itself is the essence of good traffic modelling. The second part of the thesis, studies the ON-OFF characteristics of VoIP traffic with reference to accurate measurements of the ON and OFF periods, made from a large multi-lingual database of over 100 hours worth of VoIP call recordings. The impact of the language, prosodic structure and speech rate of the speaker on the statistics of the ON-OFF periods is analysed and relevant conclusions are presented. Finally, an ON-OFF VoIP source model with log-normal transitions is contributed as an ideal candidate to model VoIP traffic and the results of this model are compared with those of previously published work.

An accurate and scalable analytical model for IEEE 802.15.4 slotted CSMA/CA networks

In this paper a Markov chain based analytical model is proposed to evaluate the slotted CSMA/CA algorithm specified in the MAC layer of IEEE 802.15.4 standard. The analytical model consists of two two-dimensional Markov chains, used to model the state transition of an 802.15.4 device, during the periods of a transmission and between two consecutive frame transmissions, respectively. By introducing the two Markov chains a small number of Markov states are required and the scalability of the analytical model is improved. The analytical model is used to investigate the impact of the CSMA/CA parameters, the number of contending devices, and the data frame size on the network performance in terms of throughput and energy efficiency. It is shown by simulations that the proposed analytical model can accurately predict the performance of slotted CSMA/CA algorithm for uplink, downlink and bi-direction traffic, with both acknowledgement and non-acknowledgement modes.

Unified model for performance analysis of IEEE 802.11 ad hoc networks in unsaturated conditions

IEEE 802.11 standard has achieved huge success in the past decade and is still under development to provide higher physical data rate and better quality of service (QoS). An important problem for the development and optimization of IEEE 802.11 networks is the modeling of the MAC layer channel access protocol. Although there are already many theoretic analysis for the 802.11 MAC protocol in the literature, most of the models focus on the saturated traffic and assume infinite buffer at the MAC layer. In this paper we develop a unified analytical model for IEEE 802.11 MAC protocol in ad hoc networks. The impacts of channel access parameters, traffic rate and buffer size at the MAC layer are modeled with the assistance of a generalized Markov chain and an M/G/1/K queue model. The performance of throughput, packet delivery delay and dropping probability can be achieved. Extensive simulations show the analytical model is highly accurate. From the analytical model it is shown that for practical buffer configuration (e.g. buffer size larger than one), we can maximize the total throughput and reduce the packet blocking probability (due to limited buffer size) and the average queuing delay to zero by effectively controlling the offered load. The average MAC layer service delay as well as its standard deviation, is also much lower than that in saturated conditions and has an upper bound. It is also observed that the optimal load is very close to the maximum achievable throughput regardless of the number of stations or buffer size. Moreover, the model is scalable for performance analysis of 802.11e in unsaturated conditions and 802.11 ad hoc networks with heterogenous traffic flows. © 2012 KSI.

Fluctuation-induced traffic congestion in heterogeneous networks

In studies of complex heterogeneous networks, particularly of the Internet, significant attention was paid to analyzing network failures caused by hardware faults or overload, where the network reaction was modeled as rerouting of traffic away from failed or congested elements. Here we model another type of the network reaction to congestion - a sharp reduction of the input traffic rate through congested routes which occurs on much shorter time scales. We consider the onset of congestion in the Internet where local mismatch between demand and capacity results in traffic losses and show that it can be described as a phase transition characterized by strong non-Gaussian loss fluctuations at a mesoscopic time scale. The fluctuations, caused by noise in input traffic, are exacerbated by the heterogeneous nature of the network manifested in a scale-free load distribution. They result in the network strongly overreacting to the first signs of congestion by significantly reducing input traffic along the communication paths where congestion is utterly negligible. © Copyright EPLA, 2012.

Modeling high-speed network traffic with truncated α-stable processes

It has been reported that high-speed communication network traffic exhibits both long-range dependence (LRD) and burstiness, which posed new challenges in network engineering. While many models have been studied in capturing the traffic LRD, they are not capable of capturing efficiently the traffic impulsiveness. It is desirable to develop a model that can capture both LRD and burstiness. In this letter, we propose a truncated a-stable LRD process model for this purpose, which can characterize both LRD and burstiness accurately. A procedure is developed further to estimate the model parameters from real traffic. Simulations demonstrate that our proposed model has a higher accuracy compared to existing models and is flexible in capturing the characteristics of high-speed network traffic. © 2012 Springer-Verlag GmbH.

Language independent on-off voice over IP source model with lognormal transitions

The recent explosive growth of voice over IP (VoIP) solutions calls for accurate modelling of VoIP traffic. This study presents measurements of ON and OFF periods of VoIP activity from a significantly large database of VoIP call recordings consisting of native speakers speaking in some of the world's most widely spoken languages. The impact of the languages and the varying dynamics of caller interaction on the ON and OFF period statistics are assessed. It is observed that speaker interactions dominate over language dependence which makes monologue-based data unreliable for traffic modelling. The authors derive a semi-Markov model which accurately reproduces the statistics of composite dialogue measurements. © The Institution of Engineering and Technology 2013.