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Related Experiment Video

Updated: Nov 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds.

Xianzhong Tian¹, Juan Zhu¹, Ting Xu¹

¹School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)

|January 6, 2021

Summary

This study introduces a mobility-aware Deep Neural Network (DNN) partitioning algorithm (MDPO) to reduce latency and energy use on mobile devices. MDPO effectively manages DNN computation offloading, adapting to user movement for improved performance.

Keywords:

deep neural networks mobile edge computing mobility management partition offloading

Related Experiment Videos

Last Updated: Nov 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Artificial Intelligence
Mobile Computing
Edge Computing

Background:

Deep Neural Networks (DNNs) offer high performance but are resource-intensive for mobile devices, causing latency and energy issues.
Centralized cloud DNN processing requires large data transfers and introduces significant latency.
Edge cloud offloading is a solution, but mobile device mobility can lead to computation offloading failures.

Purpose of the Study:

To develop a novel algorithm, Mobility-Included DNN Partition Offloading (MDPO), to address DNN computation offloading challenges for mobile users.
To minimize the total latency of completing a DNN job while accounting for user mobility.
To ensure the algorithm's suitability for various DNN topologies, including chain and graphic structures.

Main Methods:

Developed the Mobility-Included DNN Partition Offloading (MDPO) algorithm, integrating user mobility considerations into DNN partitioning and offloading strategies.
Designed MDPO to adapt dynamically to changing network conditions and user movement patterns.
Evaluated MDPO's performance against local-only and edge-only execution benchmarks.

Main Results:

MDPO significantly reduces the total latency for completing DNN jobs compared to traditional local-only and edge-only execution methods.
The algorithm demonstrates improved overall DNN performance by efficiently managing computation offloading.
MDPO exhibits strong adaptability to diverse network conditions, maintaining performance even with varying connectivity.

Conclusions:

The proposed MDPO algorithm effectively mitigates latency and energy consumption issues associated with running DNNs on mobile devices.
MDPO provides a robust solution for mobile DNN computation offloading, successfully addressing the challenges posed by user mobility.
The algorithm's adaptability makes it a valuable tool for optimizing intelligent applications in dynamic mobile environments.