Optimal Resource Allocation for Crowdsourced Image Processing

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

doi:10.1109/SECON48991.2020.9158417

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 proceeding › Conference contribution

3 Scopus citations

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 language	English (US)
Title of host publication	2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728166308
DOIs	https://doi.org/10.1109/SECON48991.2020.9158417
State	Published - Jun 2020
Event	17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy Duration: Jun 22 2020 → Jun 25 2020

Publication series

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

Conference

Conference	17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Country/Territory	Italy
City	Virtual, Online
Period	6/22/20 → 6/25/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/SECON48991.2020.9158417

Cite this

Wheatman, K. S., Mehmeti, F., Mahon, M., Qiu, H., Chan, K., & La Porta, T. (2020). Optimal Resource Allocation for Crowdsourced Image Processing. In 2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 Article 9158417 (Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops). IEEE Computer Society. https://doi.org/10.1109/SECON48991.2020.9158417

@inproceedings{407aa070b5404096900b0904739b5d6d,

title = "Optimal Resource Allocation for Crowdsourced Image Processing",

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.",

author = "Wheatman, \{Kristina Sorensen\} and Fidan Mehmeti and Mark Mahon and Hang Qiu and Kevin Chan and \{La Porta\}, Thomas",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 ; Conference date: 22-06-2020 Through 25-06-2020",

year = "2020",

month = jun,

doi = "10.1109/SECON48991.2020.9158417",

language = "English (US)",

series = "Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops",

publisher = "IEEE Computer Society",

booktitle = "2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020",

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}

Wheatman, KS, Mehmeti, F, Mahon, M, Qiu, H, Chan, K & La Porta, T 2020, Optimal Resource Allocation for Crowdsourced Image Processing. in 2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020., 9158417, Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, IEEE Computer Society, 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020, Virtual, Online, Italy, 6/22/20. https://doi.org/10.1109/SECON48991.2020.9158417

Optimal Resource Allocation for Crowdsourced Image Processing. / Wheatman, Kristina Sorensen; Mehmeti, Fidan; Mahon, Mark et al.
2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020. IEEE Computer Society, 2020. 9158417 (Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimal Resource Allocation for Crowdsourced Image Processing

AU - Wheatman, Kristina Sorensen

AU - Mehmeti, Fidan

AU - Mahon, Mark

AU - Qiu, Hang

AU - Chan, Kevin

AU - La Porta, Thomas

PY - 2020/6

Y1 - 2020/6

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85091997522

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U2 - 10.1109/SECON48991.2020.9158417

DO - 10.1109/SECON48991.2020.9158417

M3 - Conference contribution

AN - SCOPUS:85091997522

T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops

BT - 2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020

PB - IEEE Computer Society

T2 - 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020

Y2 - 22 June 2020 through 25 June 2020

ER -

Wheatman KS, Mehmeti F, Mahon M, Qiu H, Chan K, La Porta T. Optimal Resource Allocation for Crowdsourced Image Processing. In 2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020. IEEE Computer Society. 2020. 9158417. (Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops). doi: 10.1109/SECON48991.2020.9158417

Optimal Resource Allocation for Crowdsourced Image Processing

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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