Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.

Efficient road detection and tracking for unmanned aerial vehicle

Abstract - An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground–vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut–based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 x 595 videos on average.

An embedding scheme for detecting anomalous block structured graphs

Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model.

2Loud? Monitoring traffic noise with mobile phones

The World Health Organization has recently focused attention on guidelines for night noise in urban areas, based on significant medical evidence of the adverse impacts of exposure to excessive traffic noise on health, especially caused by sleep disturbance. This includes serious illnesses, such as hypertension, arteriosclerosis and myocardial infarction. 2Loud? is a research project with the aim of developing and testing a mobile phone application to allow a community to monitor traffic noise in their environment, with focus on the night period and indoor measurement. Individuals, using mobile phones, provide data on characteristics of their dwellings and systematically record the level of noise inside their homes overnight. The records from multiple individuals are sent to a server, integrated into indicators and shared through mapping. The 2Loud? application is not designed to replace existing scientific measurements, but to add information which is currently not available. Noise measurements to assist the planning and management of traffic noise are normally carried out by designated technicians, using sophisticated equipment, and following specific guidelines for outdoors locations. This process provides very accurate records, however, for being a time consuming and expensive system, it results in a limited number of locations being surveyed and long time between updates. Moreover, scientific noise measurements do not survey inside dwellings. In this paper we present and discuss the participatory process proposed, and currently under implementation and test, to characterize the levels of exposure to traffic noise of residents living in the vicinity of highways in the City of Boroondara (Victoria, Australia) using the 2Loud? application.

Monitoring the marine environment adjacent to a petroleum refinery on Corio Bay, Victoria, Australia /Peter James Gilbert

The objective of the work reported in this thesis was to design and implement an ecological effects environmental monitoring program which would: 1) Collect baseline biological information on sessile epibiotic fouling communities from an area adjacent to a petroleum refinery located on Corio Bay, Victoria, to allow comparison with results of future monitoring for the assessment of long term temporal water quality trends. 2) Detect and — if possible - estimate the magnitude of any influence on epibiotic fouling communities within the Corio Bay marine ecosystem attributable to operations at the Shell Petroleum Refinery. 3) Investigate the extent of thermal stratification and rate of dispersal of the petroleum refinery main cooling-water outfall plume (discharging up to 350,000 tonnes of warmed seawater per day), and its effect on epibiotic communities within the EPA-defined mixing zone. A major component of the work undertaken was the design and development of artificial-substrate biological sampling stations suitable for use under the conditions prevailing in Corio Bay, and the development of appropriate quantitative underwater photographic sampling techniques to fulfil the experimental criteria outlined above. Experimental and other constraints imposed on the design of the stations precluded the simple suspension of frames from jetties or pylons, a technique widely used in previous work of this type. Artificial substrate panels were deployed along three radial transects centred within and extending beyond the petroleum refinery main cooling-water mixing zone. Identical substrate panels were deployed at a number of control sites located throughout Corio Bay, each chosen for differences in their degree of exposure to such factors as water movement, depth, shipping traffic and/or comparable industrial activity. The rate of colonisation (space utilisation) and the development of epibiotic fouling communities on artificial substrate panels was monitored over two twelve-month sampling periods using quantitative underwater photographic sampling techniques. Sampling was conducted at 4-8 week intervals with the rate of panel colonisation and community structure determined via coverage measurements. Various species of marine algae, polychaete tubeworms, hydroids, barnacles, simple and colonial ascidians, sponges, bivalve molluscs and encrusting bryozoans were all detected growing on panels. Communities which established on panels within the cooling-water mixing-zone and those at control sites were compared using statistical procedures including agglomerative hierarchical cluster analysis. A photographic sample archive has been established to allow comparison with similar future studies.

Network traffic classification using correlation information

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

Classifying network traffic in the big data era

With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.

2Loud?: Community mapping of exposure to traffic noise with mobile phones

Abstract: Despite ample medical evidence of the adverse impacts of traffic noise on health, most policies for traffic noise management are arbitrary or incomplete, resulting in serious social and economic impacts. Surprisingly, there is limited information about citizen’s exposure to traffic noise worldwide. This paper presents the 2Loud? mobile phone application, developed and tested as a methodology to monitor, assess and map the level of exposure to traffic noise of citizens with focus on the night period and indoor locations, since sleep disturbance is one of the major triggers for ill health related to traffic noise. Based on a community participation experiment using the 2Loud? mobile phone application in a region close to freeways in Australia, the results of this research indicates a good level of accuracy for the noise monitoring by mobile phones and also demonstrates significant levels of indoor night exposure to traffic noise in the study area. The proposed methodology, through the data produced and the participatory process involved, can potentially assist in planning and management towards healthier urban environments.

On addressing the imbalance problem : A correlated KNN approach for network traffic classification

With the arrival of big data era, the Internet traffic is growing exponentially. A wide variety of applications arise on the Internet and traffic classification is introduced to help people manage the massive applications on the Internet for security monitoring and quality of service purposes. A large number of Machine Learning (ML) algorithms are introduced to deal with traffic classification. A significant challenge to the classification performance comes from imbalanced distribution of data in traffic classification system. In this paper, we proposed an Optimised Distance-based Nearest Neighbor (ODNN), which has the capability of improving the classification performance of imbalanced traffic data. We analyzed the proposed ODNN approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments were implemented on the real-world traffic dataset. The results show that the performance of “small classes” can be improved significantly even only with small number of training data and the performance of “large classes” remains stable.

Wireless-based sleep technology for monitoring sleep apnea

Abstract: This paper covers the technical aspects of the wireless-based sleep technology for monitoring sleep apnea, which is a sleep disorder that can be detected via continuous monitoring. In this paper, a wireless system testbed is designed to monitor the patients for the signs of sleep apnea. The testbed is comprised of a number of biomedical sensors, which are used to monitor the related biological parameters related to the patient's sleeping mechanism, such as: nasal airflow, snoring, abdominal, leg, chest, and eye movements, blood oxygen level, blood pressure, and body position. The goal of this paper is to characterize the and model the data traffic generated from the biomedical sensors used in the sleep apnea study and find the network-centric lower traffic limits; minimum frequency deployment and minimum bandwidth required.