Zhou Peng, Chen Wei, Wang Jing, Wang Huan, Zhang Yunfeng, Cao Bingyu, Sun Shan, He Lina
School of Information Science and Engineering, Xinjiang College of Science & Technology, Korla, 841000, Xinjiang, China.
School of Computer, North China Institute of Aerospace Engineering, Langfang, 065000, Hebei, China.
Sci Rep. 2024 Jul 20;14(1):16728. doi: 10.1038/s41598-024-66631-8.
The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine-cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.
农业物联网（IoT）已通过提供实时监测和警报功能，广泛应用于协助梨农进行病虫害预测以及精准作物管理。为提高农业物联网监测应用的有效性，聚类协议被用于农业无线传感器网络（AWSN）的数据传输。然而，簇头的选择是一个NP难问题，传统算法无法有效解决。基于此，本文提出一种新颖的AWSN聚类模型，该模型综合考虑了节点能量、节点度、平均距离和延迟等多个因素。此外，还提出了一种基于高斯变异和正弦余弦萤火虫算法（GSHFA-HCP）的新型高性能聚类协议，以满足不同场景的实际需求。引入创新的高斯变异策略和正弦余弦混合策略来有效优化聚类方案。此外，基于节点间距离、剩余能量和负载设计了一种高效的簇间数据传输机制。实验结果表明，与其他四种流行方案相比，所提出的GSHFA-HCP协议在降低网络能耗、延长网络寿命和减少传输延迟方面具有显著的性能提升。与其他协议相比，GSHFA-HCP在网络寿命、吞吐量、传输延迟和丢包率方面的优化率分别达到63.69%、17.2%、19.56%和35.78%。
