SENSELET: a Sensory Network infrastructure for Scientific Lab Environments

People

  • Klara Nahrstedt:   Principle Investigator
  • John Dallesasse:   Co-Principle Investigator
  • Tracy Smith:          Co-Principle Investigator
  • Roy Campbell:       Co-Principle Investigator
  • Kenton McHenry: Co-Principle Investigator
  • Hessam Moeini:    Postdoc Researcher
  • Zhe Yang:               PhD Student
  • Tuo Yu:                   PhD Student
  • Xiaoyang Wang:   PhD Student
  • Beitong Tian:         PhD Student
  • Patrick Su:              PhD Student
  • Robert Kaufman:  PhD Student

Timeline

  • Fall 2018-present

Project Description

The cloud service 4Ceed and the edge device BRACELET accelerates the process of making scientific discoveries by providing researchers with the convenience to upload, examine, and process their experimental data (e.g., microscope images) and metadata (e.g., microscope settings). For researchers, an equally important information towards correct scientific experimentation, besides instrument raw data and metadata, is  sensory data around the instruments when experiments are conducted. For example, the ability to capture and control laboratory environmental sensory information such as temperature, humidity, vibration is crucial for nanofabrication. In some laboratories we have few stand-alone sensors to collect humidity data. However, it is very time consuming to manually collect and correlate those parameters with our fabrication process.

We design a sensor network architecture, SENSELET, for scientific lab environments. Our goal is to (a) deploy a diverse wireless and scalable sensory infrastructure in experimental labs, close to scientific instruments, and (b) correlate and synchronize sensory data with cloud-based instrument data and metadata in real-time and on-demand. SENSELET infrastructure will provide additional contextual measurements that will increase accuracy and causal relations of scientific results for scientists, and better environmental monitoring and control of scientific labs for lab managers.

Publications

  • Zhe Yang, Phuong Nguyen, Haiming Jin, Klara Nahrstedt, “MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows”, the 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019), Dallas, USA, July 2019.
  • USING COMPUTER SCIENCE TO ASSIST IN CREATING AN ULTRA-CLEAN SCIENTIFIC LAB ENVIRONMENT (This article was produced by Futurum, a magazine and online platform aimed at inspiring young people to follow a career in the sciences, research and technology. For more information, teaching resources, and course and career guides, see www.futurumcareers.com)

Funding Agencies

This research project is funded by the National Science Foundation (award number 1827126).