Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

• Published in: Journal of network and computer applications Vol. 217; p. 103674
• Main Authors: Wang, Ying, Shang, Fengjun, Lei, Jianjun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Deep reinforcement learning; Duty cycling; Lyapunov optimization; Routing optimization; Wireless sensor network; Wireless sensor network; Lyapunov optimization; Deep reinforcement learning; Routing optimization; Duty cycling
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1016/j.jnca.2023.103674

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.