Parallel Processing of Images in Mobile Devices using BOINC

Author:

Curiel Mariela,Calle David F.,Santamaría Alfredo S.,Suarez David F.,Flórez Leonardo

Abstract

Abstract Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. A mobile grid can be an adequate computing infrastructure for this problem. A mobile grid is a grid that includes mobile devices as resource providers. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. This article presents answers to these four challenges.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering,Environmental Engineering

Reference72 articles.

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1. Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System;Physics of Particles and Nuclei;2024-06

2. Mobile crowd computing: potential, architecture, requirements, challenges, and applications;The Journal of Supercomputing;2023-07-29

3. Design and Analysis of Proposed Smartphone-based Distributed Parallel Processing System;2023 9th International Engineering Conference on Sustainable Technology and Development (IEC);2023-02-21

4. Challenges in Processing Medical Images in Mobile Devices;Trends and Advancements of Image Processing and Its Applications;2021-11-14

5. A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing;Symmetry;2021-09-16

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