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Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent

Nu Wen^1,2, Ying Zhou³, Yang Wang¹

¹Internet of Things Research Institute, Shenzhen Polytechnic University, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This study introduces an optimized data strategy for artificial intelligence (AI) systems in intelligent transportation, significantly reducing processing time for object recognition and detection tasks on edge computing devices.

Keywords:

artificial intelligence intelligent transportation sensor-based data

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Area of Science:

Intelligent Transportation Systems
Edge Computing
Artificial Intelligence

Background:

Intelligent transportation systems face real-time challenges in object recognition, detection, and location.
Existing AI systems struggle with efficient resource allocation and operational efficiency in edge computing environments.

Purpose of the Study:

To propose an automatic sensor-based data loading and unloading optimization strategy for AI algorithm models.
To address resource allocation optimization and improve operational efficiency in edge computing for intelligent transportation.
To meet the real-time computing requirements of intelligent transportation business applications.

Main Methods:

Implemented node and sensor management mechanisms.
Utilized efficient communication protocols for dynamic sensor-based data management.
Applied the strategy to AI models for pedestrian recognition, vehicle detection, and ship positioning.

Main Results:

Achieved significant reduction in inference time (one tenth to one twentieth) while maintaining recall rate.
Enhanced privacy protection for sensor-based data.
Demonstrated improved operational efficiency in edge computing environments.

Conclusions:

The proposed strategy effectively optimizes data loading and unloading for AI models in intelligent transportation.
The approach enhances real-time processing capabilities and data privacy.
Future work may involve distributed computing for further optimization under high load conditions.