UDC SEAS CSIT

Research Projects

The faculty and student of the Informatics Lab are simultaneously engaged in several projects. As each of these projects develops, individual pages will be added to this list documenting the current progress.

Real-time Tracking and Querying Whereabouts using Cloud Computing

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  • Environmental Urban Runoff Monitoring
    The primary idea of the project is a transformative use of the SPOT's accelerometer, Java computer, solar power, and wireless communication functions for real-time and online monitoring of runoff quantity; and the secondary idea is an investigation of transformative applications of the extensible sensor board of the SPOT and its attached/compatible sensors for future runoff quality monitoring. As a continuation of this project, a smart sensor sphere has been desiend to measure water quality in real-time.

  • Real-time Tracking and Querying Whereabouts using Cloud Computing
    Utilizing of both mobile computing and cloud computing is important to overcome the limitations of analyzing and tracking location changes in real-time. In this project, a smart device is used to track location changes through Global Positioing System (GPS). The location information is automatically tranferred to cloud computing system to perform uncertainty calculation to show the uncertainty boundaries in real-time.

  • High-performance Sensor Data Processing in Cloud Computing
    This project focuses on designing an original parallel-processing solution for efficiently managing the uncertainty using the map-reduce platform of cloud computing. Specifically, it mainly studies on undertainty computation of continuously changing data objects (CCDOs) in a cloud computing environment.

  • Cloud Computing & HPC
    As a cloud, the cluster provides the IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service) functionality. Modern cybersecurity challenges meet data-intensive (big data) challenges incurred by the huge volume of rapidly increasing (high-velocity) data produced in scientific computing, social networking and computing, and cloud computing environments. To meet the technical challenges of such data-intensive (or Big Data) trends, the Cloud also supports the DaaS (Data as a Service) functionality based on HBase, the most popular open source version of Google's proven Bigtable technology, for massive and parallel processing of very large and high-velocity data with the following distinctive advantages: (1) parallel data access for high data accessibility and high write throughput rate; (2) controlled data replica management for fault and data-loss tolerance; (3) minimum data transmission between storage and computation in parallel processing for higher computation performance and minimal energy consumption; (4) elastic platform for automatic and on-the-fly storage and computation scaling when more computers are added to the cluster on an ad hoc manner. Informatics Lab's Hadoop Cloud is a cluster of 27 computers providing parallel storage and computation capabilities. With this cloud computing facility, Informatics Lab joined to SURAgrid (http://www.suragrid.org) as a contributing member to support education and research efforts to all SURAgrid member institutions.

  • Visual Analytics on Large Network Traffic Data using Cloud Computing
    This project focuses on designing a visual analytics system for detecting anomalies in large network traffic data through interactive visual analysis. Since understanding and analyzing exponentially growing network traffic data is difficult, an efficient interactive visual analytics system is developped to explore and examine large amounts of data quickly and efficiently.

Smart Sensing Technology



Smart Sensor Sphere (SSS)

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Smart Sensor Sphere (SSS) (filed test)

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Command and Control System (CCS)

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Database System Technology for Video Observation Purposes



First System Prototype

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