Skip to main content

Unequal loss protection for collaborative intelligence

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2024-04-23
Authors/Contributors
Abstract
This thesis explores combining Unequal Loss Protection (ULP) and Forward Error Correction (FEC) techniques in Collaborative Intelligence (CI). The focus is on reliably transmitting deep-feature tensor data in edge-cloud systems. The first part of the thesis tries to highlight a lack of detailed research on ULP within CI, despite its success in other contexts such as image and video transmission. As data integrity is crucial for CI systems, this work suggests a new way to make data transmission more robust against packet loss without needing more data to be sent. This is done by replacing less important data packets with FEC codes. These packets are chosen through a proxy model that mimics the modified Grad-CAM technique. The research also looks into how independent and identically distributed (iid) packet loss affects the accuracy of transmitting deep-feature tensor data. It shows that the new ULP method works well through extensive evaluations. Additionally, the thesis explores the integration of ULP and error concealment methods, focusing not only on the importance of packets but also on their reconstructability. The goal of this approach is to enhance the effectiveness of ULP by strategically selecting which packets to protect based on both how important they are and how easily they can be reconstructed if lost. This thesis helps advance CI by introducing a new method for using ULP in deep feature tensor transmission in edge-cloud systems.
Document
Extent
44 pages.
Identifier
etd23028
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: V., Bajić, Ivan
Language
English
Member of collection
Download file Size
etd23028.pdf 13.55 MB

Views & downloads - as of June 2023

Views: 12
Downloads: 0