KENTECH Intelligent Mobile Computiong lab (IMC Lab)

Intelligent Data Collection Platforms

1. EasyTrack: A Scalable and General Purpose Platform for Reliable Data Collection in mHealth Studies

EasyTrack is a meticulously crafted software solution that addresses the increasing demand for large-scale longitudinal data collection in mHealth applications. It serves the needs of the academic and medical communities by offering a versatile platform capable of accommodating diverse participant populations and their associated sensor data. EasyTrack stands out for its general-purpose functionality, making it adaptable for a wide range of mHealth studies. Additionally, it incorporates a robust Data Quality (DQ) monitoring mechanism, featuring a user-friendly dashboard and automated problem-detection routines that promptly alert researchers to potential data discrepancies. Furthermore, EasyTrack ensures seamless interoperability with existing data collection platforms.

2. Community Energy Management System (CEMS)

The Community Energy Management System (CEMS) is an innovative venture designed to streamline and fine-tune energy utilization across our campus, encompassing research facilities, administrative buildings, and residential areas. Recognizing the unique energy consumption patterns inherent to each building type, CEMS is developed to align its management strategies with the specific energy signatures of each establishment. Central to our academic exploration in the realm of CEMS is the integration of fast demand response (fast DR) mechanisms. The methodology commences with the identification of pivotal, controllable loads that directly influence campus energy consumption. By leveraging very short-term load forecasting techniques differentiated by time granularity, we can respond to and manage energy demands in an immediate and dynamic fashion. Our research can support user satisfaction and experiential input while stressing fast, efficient energy management. As a result, we cannot forge ahead with improvements in energy efficiency while also ensuring the overall comfort and well-being of users.

3. Campus Open Energy Platform

Within the context of the KENTECH microgrid roadmap, the Campus Open Energy Data Platform emerges as a transformative force. This roadmap paves the way for sustainable energy solutions, and the platform is poised to revolutionize the way we view, analyze, and act on energy data. This state-of-the-art platform not only collects and stores vast quantities of energy-related big data, but it also offers effective data processing applications encompassing time series analysis, load forecasting, fault diagnosis, and more. With its intuitive dashboard, users can visualize diverse datasets, enabling informed decision-making. Undergraduate students can directly engage with real-time power consumption patterns, enhancing their understanding of energy management, while graduate research is bolstered with crucial data for R&D in cutting-edge energy sectors. Further, the platform is a catalyst for innovation, offering budding tech entrepreneurs the tools they need for novel energy-related ventures. Through its comprehensive offerings, the platform not only advances educational and research endeavors but also propels the broader energy sector into the future.

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  • Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services,
    EasyTrack-Orchestrating Large-scale Mobile User Experimental Studies (poster)
    Kobiljon Toshnazarov, Hamza Baazizi, Nematjon Narziev, Youngtae Noh, Uichin Lee

Research Participants

  • Research Professor
    Caceres-Najarro Lismer Andres
    Research Interest
    • Evolutionary algorithms
    • Wireless sensor networks
    • Localization and tracking
    • Smart Grid
    • Smart healthcare
  • Postdoctoral Researcher
    Muhammad Salman
    Research Interest
    • Occupancy monitoring
    • Spy camera detection
    • Wireless Networks and SDN
    • Bufferbloat mitigation
    • Contactless Stress Detection
  • PhD Student
    Kobiljon Toshnazarov
    Research Interest
    • Stress Detection
    • mHealth Data Collection
    • Digital Therapeutics (DTx)
  • Integrated PhD Student
    Lee Yong geon
    Research Interest
    • Sensor Data Science
    • Energy Behavior Modeling
    • Energy Management System
    • Digital Therapeutics