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                            Library & Multimedia
 An Open-World Time-Series Sensing Framework for Embedded Edge Devices
NCJ Number
              308377

Author(s)
          Abdulrahman Bukhari;
                        Seyedmehdi Hosseinimotlagh;
                        Hyoseung Kim
Date Published

2022
Length
              10 pages
Annotation
              This paper introduces an open-world time-series sensing framework for embedded edge devices.
Abstract
              In this paper, the authors propose an open-world time-series sensing framework for making inferences from time-series sensor data and achieving incremental learning on an embedded edge device with limited resources. The proposed framework is able to achieve two essential tasks, inference and learning, without requiring access to a powerful cloud server. The authors discuss the design choices made to ensure satisfactory learning performance and efficient resource usage. Experimental results demonstrate the ability of the system to incrementally adapt to unforeseen conditions and to effectively run on a resource-constrained device. The rapid advancement of IoT technologies has generated much interest in the development of learning-based sensing applications on embedded edge devices. However, these efforts are being challenged by the need to adapt to unforeseen conditions in an open-world environment. Updating a learning model suffers from the lack of training data as well as the high computational demand beyond that available on edge devices. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2019-NE-BX-0006


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Nanobiosensor Arrays for On-Site Multiplexed Detection of Protein Markers to Identify Forensically Relevant Body Fluids
    
  
  
Date Published: January 1, 2022
    
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