Deep reinforcement learning based sensing bidirectional nodes congestion control mechanism in wireless sensor networks

Jianjun Lei; Ying Zhou

doi:10.1117/12.2656660

20 October 2022 Deep reinforcement learning based sensing bidirectional nodes congestion control mechanism in wireless sensor networks

Jianjun Lei, Ying Zhou

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124515L (2022) https://doi.org/10.1117/12.2656660
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

In Wireless sensor networks (WSNs), congestion is one of the pressing challenges in providing high-quality services for users. As for node-level congestion, existing congestion control mechanisms usually only consider either the child node buffer queue or the parent node buffer queue, which leads to the inability to fully perceive the network state. In this article, we present a congestion control mechanism based on deep reinforcement learning (DRL) for sensing bidirectional nodes in different network scenarios. The nodes in the network sense the buffer queue length of themselves and their parent nodes to determine the network environment and then modify the transmission rate of nodes to reduce network congestion. Compared with other congestion schemes, our algorithm can not only dynamically adapt to network changes and reduce congestion, but also achieves a better tradeoff in the system packet loss rate, packet delivery rate and available buffer queue rate through extensive experiments.

Citation Download Citation

Jianjun Lei and Ying Zhou "Deep reinforcement learning based sensing bidirectional nodes congestion control mechanism in wireless sensor networks", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124515L (20 October 2022); https://doi.org/10.1117/12.2656660

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KEYWORDS

Sensor networks

Networks

Sensors

Detection and tracking algorithms

Optimization (mathematics)

Data modeling

Environmental sensing

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