Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

A New Visual Analytics toolkit for ATLAS metadata

The ATLAS experiment at the LHC has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of physics analysis and data processing. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the systems’ behaviour over a given period of time (i.e., long-term data analysis). Ensuring the stability and efficiency of functioning of complex and large-scale systems, such as those in ATLAS computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, containing a lot of metrics (hardware and software environment descriptions, network state, application parameters, user account data, errors) accumulated for more than decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms, and the domain-experts involvement in the process of ML data analysis is very limited. As a result the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by the algorithms with a high accuracy may have no sense regarding the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows to actively use human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the interface prototype of visual analytics platform (VAP) for the multidimensional data analysis of ATLAS computing metadata will be presented. The general data processing and visualization methods of the VAP prototype will be implemented and tested on the slice of ATLAS jobs metadata. As a result, a web-interface will provide ATLAS jobs interactive visual clusterization and search for non-trivial behaviour and its possible reasons. Furthermore, we will demonstrate the prototype of dynamic interactive visualization, providing the possibility of the observation of changing clustering structure in different points in time

High Energy Physics Data Popularity : ATLAS Datasets Popularity Case Study

The amount of scientific data generated by the LHC experiments has hit the exabyte scale. These data are transferred, processed and analyzed in hundreds of computing centers. The popularity of data among individual physicists and University groups has become one of the key factors of efficient data management and processing. It was actively used duringLHC Run-1 and Run-2 by the experiments for the central data processing, and allowed the optimization of data placement policies and to spread the workload more evenly over the existing computing resources. Besides the central data processing, the LHC experiments provide storage and computing resources for physics analysis to thousands of users. Taking into account the significant increase of data volume and processing time after the collider upgrade for the High Luminosity Runs (2027-2036)an intelligent data placement based on data access pattern becomes even more crucial than at the beginning of LHC. In this study we provide a detailed exploration of data popularity using ATLAS data samples. In addition, we analyze the geolocations of computing sites where the data were processed, and the locality of the home institutes of users carrying out physics analysis. Cartography visualization, based on this data, allow sthe correlation of existing data placement with physics needs, providing a better understanding of data utilization by different categories of user’s tasks

A New Visual Analytics toolkit for ATLAS metadata

The ATLAS experiment at the LHC has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of physics analysis and data processing. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the systems’ behaviour over a given period of time (i.e., long-term data analysis). Ensuring the stability and efficiency of functioning of complex and large-scale systems, such as those in ATLAS computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, containing a lot of metrics (hardware and software environment descriptions, network state, application parameters, user account data, errors) accumulated for more than decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms, and the domain-experts involvement in the process of ML data analysis is very limited. As a result the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by the algorithms with a high accuracy may have no sense regarding the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows to actively use human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the interface prototype of visual analytics platform (VAP) for the multidimensional data analysis of ATLAS computing metadata will be presented. The general data processing and visualization methods of the VAP prototype will be implemented and tested on the slice of ATLAS jobs metadata. As a result, a web-interface will provide ATLAS jobs interactive visual clusterization and search for non-trivial behaviour and its possible reasons. Furthermore, we will demonstrate the prototype of dynamic interactive visualization, providing the possibility of the observation of changing clustering structure in different points in time

A New Visual Analytics Toolkit for ATLAS Computing Metadata

The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e., long-term data analysis). Ensuring the stability and efficiency of functioning of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms, and the domain-experts involvement in the process of ML data analysis is very limited. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms with a high accuracy may have no sense regarding the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons

Evaluation of the Level-of-Detail Generator for Visual Analysis of the ATLAS Computing Metadata

The ATLAS experiment at the LHC processes, analyses and stores vast amounts of data, which is either recorded by the detector or simulated worldwide using Monte Carlo methods. ATLAS Computing metadata is generated at very high rates and volumes. The necessity to analyze this metadata is constantly increasing, since the heterogeneous, distributed and dynamically changing computing infrastructure requires sophisticated optimization decisions, made by human or/and by machines. Visual analytics is one of the methods facilitating the analysis of massive amounts of data (structured, semi-structured, and unstructured) which leverages human judgement by means of interactive visual representations. Given the huge number of ATLAS computing jobs that need to be visualized simultaneously for error investigations or other optimization processes, resources of the client application responsible for such visualization may reach its limits. Data objects that share similar feature values can be represented and visualized as a single group, thus initial large data sample would be represented at different levels of detail. This approach will also avoid client overload. In this paper we evaluate implementations of k-means-based Level-of-Detail generator method applied to the metadata of ATLAS jobs. This method is used in the visual analytics application InVEx (Interactive Visual Explorer) that is under development, and which is based on 3-dimensional interactive visualization of multidimensional data