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Collaborative Sensing-Aware Task Offloading and Resource Allocation for Integrated Sensing-Communication- and
Bangzhen Huang1, Xuwei Fan1, Shaolong Zheng1
1School of Informatics, Xiamen University, Xiamen 361005, China.
Sensors (Basel, Switzerland)
|February 13, 2025
Summary
We propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism to reduce sensing task transmission delays in Integrated Sensing, Communication, and Computation (ISCC) for the Internet of Vehicles (IoV). Our method optimizes task offloading and resource allocation, outperforming benchmarks.
Area of Science:
- Intelligent Transportation Systems
- Wireless Communications
- Distributed Computing
Background:
- Integrated Sensing, Communication, and Computation (ISCC) is crucial for the Internet of Vehicles (IoV), enabling real-time sensing, low-latency communication, and collaborative computing.
- Increasing sensing data in IoV strains limited communication resources, demanding efficient data transmission solutions.
Purpose of the Study:
- To reduce sensing task transmission delay in ISCC for IoV.
- To minimize the total processing delay of vehicular sensing tasks through optimized task offloading and resource allocation.
Main Methods:
- A Collaborative Sensing-Aware Task Offloading (CSTO) mechanism is proposed.
- A two-stage iterative optimization algorithm is designed to solve a mixed-integer nonlinear programming (MINLP) problem.
- Deep Reinforcement Learning (DRL) for task offloading decisions and convex optimization for communication resource allocation are employed iteratively.
Main Results:
- The proposed CSTO mechanism effectively reduces sensing task transmission delay.
- Simulation experiments demonstrate the superiority of the CSTO scheme over benchmark schemes under various parameter settings.
Conclusions:
- The developed two-stage optimization algorithm successfully addresses the challenges of task offloading and resource allocation in ISCC for IoV.
- The CSTO mechanism offers a promising solution for enhancing efficiency and reducing latency in vehicular networks.
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