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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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A DAG-Based Offloading Strategy with Dynamic Parallel Factor Adjustment for Edge Computing in IoV.

Wenyang Guan1, Qi Zheng1, Xiaoqin Lian1

  • 1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Dynamic adjustment of Parallel Factor (DPF) algorithm for Internet of Vehicles (IoV) task offloading. DPF optimizes resource use and reduces task completion time by dynamically adjusting task parallelism in edge computing environments.

Keywords:
Internet of Vehiclesdirected acyclic graphdynamic adjustmentmobile edge computingtask offloading

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Area of Science:

  • Intelligent transportation systems
  • Edge computing
  • Network performance optimization

Background:

  • Internet of Vehicles (IoV) generates massive data, posing challenges for computing resource allocation.
  • Dynamic and real-time IoV task characteristics necessitate adaptive offloading strategies beyond static methods.

Purpose of the Study:

  • To propose an adaptive task offloading algorithm for IoV systems.
  • To optimize resource utilization and minimize task completion time in dynamic IoV environments.

Main Methods:

  • Developed a Dynamic adjustment of Parallel Factor (DPF) algorithm for task offloading.
  • Utilized edge computing to dynamically adjust the parallel factor of directed acyclic graphs (DAGs).
  • Implemented real-time monitoring of network conditions and vehicle states for dynamic task scheduling.

Main Results:

  • The DPF algorithm significantly reduces task delay and improves task success rates compared to static strategies.
  • DPF enhances overall system efficiency and resource utilization.
  • Experiments show superior performance, especially in high-load scenarios, with markedly better task completion times.

Conclusions:

  • The proposed DPF algorithm effectively addresses the limitations of static offloading in IoV.
  • Dynamic adjustment of task parallelism is crucial for optimizing IoV systems.
  • DPF offers a promising solution for efficient and reliable task offloading in intelligent transportation.