Traffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

Population-Based Tract-To-Region Connectome of the Human Brain and Its Hierarchical Topology

Connectome maps region-to-region connectivities but does not inform which white matter pathways form the connections. Here we constructed the first population-based tract-to-region connectome to fill this information gap. The constructed connectome quantifies the population probability of a white matter tract innervating a cortical region. The results show that ~85% of the tract-to-region connectome entries are consistent across individuals, whereas the remaining (~15%) have substantial individual differences requiring individualized mapping. Further hierarchical clustering on cortical regions revealed their parcellations into dorsal, ventral, and limbic networks based on the tract-to-region connective patterns. The clustering results on white matter bundles revealed the connectome-based categorization of fiber bundle systems in the association pathways. This new tract-to-region connectome provides insights into the connective topology between cortical regions and white matter bundles. The derived hierarchical relation further offers a connectome-based categorization of gray matter and white matter structures.

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

Enhancement the Efficiency of Work with P2P System

The P2P system should be used Proximity information to minimize the load of file request and improve the efficiency of the work .Clustering peers for their physical proximity can also rise the performance of the request file. However, very few currently work in a peer group based on demands as peers on physical proximity. Although structured P2P provides more efficient files requests than unstructured P2P, it is difficult to apply because of their strictly defined topology. In this work, we intending to introduce a system for exchange a P2P file for proximity and level of interest based on structured P2P nodes that form physically block in the cluster and other groups physically close and nodes of public interest in sub-cluster based on the hierarchical topology. Querying an effective file is important for the overall P2P file exchange performance. Clustering peers from their common interests can significantly enhance the efficiency of the request file PAIS use an intelligent file replication algorithm to further rise the efficiency of the request file .Create a copy file that is often requested by a group of physically close nodes in their position. In addition, PAIS improves the search for files within the intra-system sub-cluster through various approaches. First, it further classifies interest in the sub-cluster to a number of subsections of interests and groups with common interest-free sub nodes in the group for file sharing. Secondly PAIS creates an over for each group that connects nodes of less node capacity to a higher throughput for the distributed node overload prevention request file. Third, in order to reduce the search for late files, PAIS uses a set of proactive information so that applicant can file knowledge if its requested file is in the neighboring nodes. Fourth, reduce the overhead of collecting information about files using the PAIS, collection of file information based on the Bloom Filter and the corresponding search for files distributed. Fifth, in order to improve the efficiency of file sharing, PAIS ranks the results with a blob of filters in order. Sixth, while the newly visited file is usually re-visited approach, based on the Bloom filter is improved only through the management of new information flowering filter is added to reduce the delay of file search. The experimental result of the Real-world Planet Lab Experiment shows that PAIS significantly reduces overhead and improves the efficiency of scrolling and without sharing files. In addition, the experimental results show high efficiency within the sub-research cluster of file approaches to improve file search efficiency.

A Survey of Chain Cluster-Based Routing Protocols in Wireless Sensor Network

Over a period, Wireless sensor networks (WSNs) have turned out to be continuously increasingly appealing and discovered the way into a wide assortment of uses and also frameworks in view of their relatively inexpensive, self-sustaining ability, and their detecting capacity in harsh environment. It is a collection of hubs composed onto a system. Based on their routing architecture they are generally separated into two classes: Hierarchical routing topology and Flat routing topology. In a flat routing topology every nodes will have the similar use and they perform similar task presented by the network. Nodes present in hierarchical topology carry out unique tasks and they are normally formed into clusters in WSNs. In this paper, a detailed study is made on chain cluster-based routing schemes for WSNs and few comparisons made by considering performance features such as Fault tolerance, scalability and topology Strengths and limitations of each scheme and the same is presented.