Scheduling shared data acquisition for real-time decision making

Tai Sheng Cheng, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates scheduling policies for the acquisition of possibly overlapping sets of data items required to make multiple decisions by different deadlines. The work is motivated by military IoT applications in which a large number of sensors must collect intelligence data to make multiple decisions. For example, data from several cameras in a large city might be needed to decide where targets of interest are. We assume that network bandwidth is limited, creating a significant resource bottleneck (perhaps between sensors and the command center where decisions are made). This might be the case, for example, due to active interference by an adversary. We begin by introducing some of the observations and properties of the data acquisition scheduling problem followed by a heuristic algorithm based on the insights gained from these observations. Finally, we evaluate the new algorithm across multiple parameters and compare the results with previous heuristics, demonstrating improved performance of our solution.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131979
DOIs
StatePublished - Aug 2019
Event25th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019 - Hangzhou, China
Duration: Aug 18 2019Aug 21 2019

Publication series

NameProceedings - 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019

Conference

Conference25th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2019
Country/TerritoryChina
CityHangzhou
Period8/18/198/21/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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