•  
  •  
 

Journal of System Simulation

Abstract

Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel states and surveillance task information from multi-slot state sequences. By capturing the dependency relationships between multi-slot state sequences, the representation ability of network state and the robustness of the algorithm are improved. Experimental results show that the proposed algorithm outperforms traditional reinforcement learning algorithm and heuristic algorithm in improving recognition accuracy and reducing task computation delay.

First Page

2113

Last Page

2126

CLC

TP391.9

Recommended Citation

Li Chao, Li Jiabao, Ding Caichang, et al. Edge Surveillance Task Offloading and Resource Allocation Algorithm Based on DRL[J]. Journal of System Simulation, 2024, 36(9): 2113-2126.

Corresponding Author

Ding Caichang

DOI

10.16182/j.issn1004731x.joss.23-0576

Download

Share

COinS