Title: An edge computing offloading strategy based on multi-dimensional attributes and distributed deep learning

Authors: Dong She

Addresses: College of Computer and Art, Anhui Technical College of Industry and Economy, Hefei, 230051, China

Abstract: With the explosive growth of smart terminal devices and the wide adoption of latency-sensitive applications, the traditional cloud computing model is difficult to meet the demands of ultra-low latency and high privacy protection. Therefore, this paper proposes a distributed deep reinforcement learning offloading strategy based on multi-dimensional joint modelling of task, device, and environment attributes. A state space integrating multi-dimensional attributes is constructed to achieve comprehensive awareness of the system state; a distributed asynchronous deep Q-network framework is designed to realise knowledge sharing through local model co-training among multiple edge nodes. Experimental results show that this approach can reduce the average task processing latency by 4.8% and the overall system energy consumption by 5.3%. This research provides a practical solution for computational offloading in resource-constrained edge scenarios that balances efficiency and energy consumption.

Keywords: multi-dimensional attributes; distributed; deep learning; edge computing offloading.

DOI: 10.1504/IJSNET.2026.151235

International Journal of Sensor Networks, 2026 Vol.50 No.1, pp.61 - 71

Received: 04 Sep 2025
Accepted: 16 Sep 2025
Published online: 19 Jan 2026 *

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