Optimal Resource Allocation for Crowdsourced Image Processing

Kristina Sorensen Wheatman, Fidan Mehmeti, Mark Mahon, Hang Qiu, Kevin Chan, Thomas La Porta

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

Abstract

Crowdsourced image processing has the potential to vastly impact response timeliness in various emergency situations. Because images can provide extremely important information regarding an event of interest, sending the right images to an analyzer as soon as possible is of crucial importance. In this paper, we consider the problem of optimally assigning resources, both local (CPUs in phones) and remote (network-based GPUs) to mobile devices for processing images, ultimately sending those of interest to a centralized entity while also accounting for the energy consumption. To that end, we use the Network Utility Maximization (NUM) framework, coupled with a hit-ratio estimator and energy costs, to enable a distributed implementation of the system. Our results are validated using both synthetic simulations and real-life traces.

Original languageEnglish (US)
Title of host publication2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728166308
DOIs
StatePublished - Jun 2020
Event17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy
Duration: Jun 22 2020Jun 25 2020

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
CountryItaly
CityVirtual, Online
Period6/22/206/25/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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