Epidemiological and evolutionary inference of the transmission network of the 2014 highly pathogenic Avian Influenza H5N2 outbreak in British Columbia, Canada

The first North American outbreak of highly pathogenic avian influenza (HPAI) involving a virus of Eurasian A/goose/Guangdong/1/1996 (H5N1) lineage began in the Fraser Valley of British Columbia, Canada in late November 2014. A total of 11 commercial and 1 non-commercial (backyard) operations were infected before the outbreak was terminated. Control measures included movement restrictions that were placed on a total of 404 individual premises, 150 of which were located within a 3 km radius of an infected premise(s) (IP). A complete epidemiological investigation revealed that the source of this HPAI H5N2 virus for 4 of the commercial IPs and the single non-commercial IP likely involved indirect contact with wild birds. Three IPs were associated with the movement of birds or service providers and localized/environmental spread was suspected as the source of infection for the remaining 4 IPs. Viral phylogenies, as determined by Bayesian Inference and Maximum Likelihood methods, were used to validate the epidemiologically inferred transmission network. The phylogenetic clustering of concatenated viral genomes and the median-joining phylogenetic network of the viruses supported, for the most part, the transmission network that was inferred by the epidemiologic analysis.

SAT-GRD: An ID/Loc split network architecture interconnecting satellite and ground networks

Since the satellite network plays an irreplaceable role in many fields, how to interconnect it with the ground network has received an unprecedented attention. However, with much more requirements imposed to the current terrestrial network, many serious problems caused by the IP dual-role exposed. In this context, their direct interconnection seems not the most appropriate way. Thus, in this paper, SAT-GRD, an incrementally deployable ID/Loc split network architecture is proposed, aiming to integrate the satellite and ground networks efficiently. Specifically, SAT-GRD separates the identity of both the host and network from the location. Then, it isolates the host from the network, and further divides the whole network into core and edge networks. These make SAT-GRD much more flexible and scalable to achieve heterogeneous network convergence and avoid problems resulting from the overloaded semantics of IP addresses. In addition, much work has been done to implement the proof-of-concept prototype of SAT-GRD, and experimental results prove its feasibility.

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.

Communication Architecture Design for Real-Time Networked Control Systems

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs