Analysis and comparison of network performance with different network parameters

The purpose of this study was to analyze the network performance by observing the effect of varying network size and data link rate on one of the most commonly found network configurations. Computer networks have been growing explosively. Networking is used in every aspect of business, including advertising, production, shipping, planning, billing, and accounting. Communication takes place through networks that form the basis of transfer of information. The number and type of components may vary from network to network depending on several factors such as requirement and actual physical placement of the networks. There is no fixed size of the networks and they can be very small consisting of say five to six nodes or very large consisting of over two thousand nodes. The varying network sizes make it very important to study the network performance so as to be able to predict the functioning and the suitability of the network. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected significantly due to the increase in the size of the network and that there exists a correlation between the various parameters and the size of the network. These variations are not only dependent on the magnitude of the change in the actual physical area of the network but also on the data link rate used to connect the various components of the network.

Simulation and analysis of network traffic for efficient and reliable information transfer

With the growing commercial importance of the Internet and the development of new real-time, connection-oriented services like IP-telephony and electronic commerce resilience is becoming a key issue in the design of TP-based networks. Two emerging technologies, which can accomplish the task of efficient information transfer, are Multiprotocol Label Switching (MPLS) and Differentiated Services. A main benefit of MPLS is the ability to introduce traffic-engineering concepts due to its connection-oriented characteristic. With MPLS it is possible to assign different paths for packets through the network. Differentiated services divides traffic into different classes and treat them differently, especially when there is a shortage of network resources. In this thesis, a framework was proposed to integrate the above two technologies and its performance in providing load balancing and improving QoS was evaluated. Simulation and analysis of this framework demonstrated that the combination of MPLS and Differentiated services is a powerful tool for QoS provisioning in IP networks.

Network approaches for the analysis of resting state fMRI data

Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”, provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila serie temporali per ogni individuo, da più di 100 valori ciascuna. I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati, così come ogni passo della costruzione del network, richiede particolare attenzione. Per ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato tramite software appositi, e anche con nuovi metodi da noi implementati. Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione delle proprietà del network, in particolare delle misure di centralità, dal momento che pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori di centralità significativi, quando confrontati con un modello nullo. Questo comportamento `e stato investigato anche a istanti di tempo diversi, usando un approccio sliding window, applicando un test statistico basato su un modello nullo pi`u complesso. Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani per mezzo della teoria dei network.

Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach

Acknowledgement SN and SS gratefully acknowledge the financial support from Lloyd’s Register Foundation Centre during this work.

Selection of a network of large lakes and reservoirs suitable for global environmental change analysis using Earth Observation

The GloboLakes project, a global observatory of lake responses to environmental change, aims to exploit current satellite missions and long remote-sensing archives to synoptically study multiple lake ecosystems, assess their current condition, reconstruct past trends to system trajectories, and assess lake sensitivity to multiple drivers of change. Here we describe the selection protocol for including lakes in the global observatory based upon remote-sensing techniques and an initial pool of the largest 3721 lakes and reservoirs in the world, as listed in the Global Lakes and Wetlands Database. An 18-year-long archive of satellite data was used to create spatial and temporal filters for the identification of waterbodies that are appropriate for remote-sensing methods. Further criteria were applied and tested to ensure the candidate sites span a wide range of ecological settings and characteristics; a total 960 lakes, lagoons, and reservoirs were selected. The methodology proposed here is applicable to new generation satellites, such as the European Space Agency Sentinel-series.

Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Healthcare Network Modeling and Analysis

Part 20: Health and Care Networks

Financial Statement Analysis for Enterprise Network Design

Part 8: Business Strategies Alignment

Analysis of a stochastic SIR epidemic on a random network incorporating household structure

This paper is concerned with a stochastic SIR (susceptible-infective-removed) model for the spread of an epidemic amongst a population of individuals, with a random network of social contacts, that is also partitioned into households. The behaviour of the model as the population size tends to infinity in an appropriate fashion is investigated. A threshold parameter which determines whether or not an epidemic with few initial infectives can become established and lead to a major outbreak is obtained, as are the probability that a major outbreak occurs and the expected proportion of the population that are ultimately infected by such an outbreak, together with methods for calculating these quantities. Monte Carlo simulations demonstrate that these asymptotic quantities accurately reflect the behaviour of finite populations, even for only moderately sized finite populations. The model is compared and contrasted with related models previously studied in the literature. The effects of the amount of clustering present in the overall population structure and the infectious period distribution on the outcomes of the model are also explored.

Performance analysis of a grid-connected PV system in LV distribution network

Over the past decade, the growing demand of Grid-connected photo voltaic (GCPV) system has been increasing due to an extensive use of renewable energy technologies for sustainable power generation and distribution. High-penetrated GCPV systems enhance the operation of the network by improving the voltage levels and reducing the active power losses along the length of the feeder. This paper aims to investigate the voltage variations and Total Harmonic Distortion (THD) of a typical GCPV system modelled in Power system simulator, PSS SINCAL with the change of level of PV integrations in a Low Voltage (LV) distribution network. Five different case studies are considered to investigate the impact of PV integrations on LV nodes and the corresponding voltage variations and harmonics. In addition, this paper also explores and benchmarks the voltage improvement techniques by implementing On Load Tap Changer (OLTC) with respective to the main transformer and addition of Shunt Capacitor (SC) at appropriate node points in LV network,

Real-time traffic flow statistical analysis based on network-constrained moving object trajectories

In this paper, we propose a novel traffic flow analysis method, Network-constrained Moving Objects Database based Traffic Flow Statistical Analysis (NMOD-TFSA) model. By sampling and analyzing the spatial-temporal trajectories of network constrained moving objects, NMOD-TFSA can get the real-time traffic conditions of the transportation network. The experimental results show that, compared with the floating-car methods which are widely used in current traffic flow analyzing systems, NMOD-TFSA provides an improved performance in terms of communication costs and statistical accuracy.

Energy-efficiency analysis of cluster-based routing protocols in wireless sensor network

This paper extends the conditions of the cluster-based routing protocols in terms of general algorithm complexity of data fusion, general compressing ratio of data fusion, and network area with long distance. Corresponding three general evaluation methods to evaluate the energy efficiency of the cluster-based routing protocols such as LEACH, PEGASIS, and BCDCP are provided. Moreover, three facts are found in them: (1) High-level software energy macro model is used to compute the energy dissipation of general data fusion software and make the constant value of energy dissipation of 1-bit data fusion an especial instance. (2) Multi-hop energy efficiency is related to the radio hardware parameters and the dynamic topology of network and the above protocols do not exploit the best use of the energy efficiency of multi-hop scheme. (3) High-energy dissipation non-cluster-head nodes, whose number changes with the density of the sensor nodes in clusters, worsen the death of nodes. The numerical results of experiments reprove these discoveries. Furthermore, they provide helpful guide for improving the above routing protocols to extent their application ranges.