A Scheme for Pest-Dense Area Localization With Solar Insecticidal Lamps Internet of Things Under Asymmetric Links

Yuan Li;Bangsong Du;Lin Luo;Yusheng Luo;Xing Yang;Ye Liu;Lei Shu
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引用次数: 1

Abstract

The combination of solar insecticidal lamps (SILs) and wireless sensor networks has spawned a green and efficient solution for agricultural pest control, called solar insecticidal lamps Internet of Things (SIL-IoTs). In realistic large-scale SIL-IoTs deployment scenarios, the integrated pest information collected across the network enables effective localization of pest-dense areas. However, the problem of asymmetric links caused by various factors, such as irregular wireless communication range and discharge interference of nodes, is the main obstacle to the deployment of SIL-IoTs. Motivated by this problem, the pest-dense area localization strategy (PALS) based on asymmetric links is proposed. First, the asymmetric nodes in the network are judged by analyzing the one-hop and two-hop information of SIL nodes. Subsequently, the Gabriel graph or relative neighborhood graph planarization algorithm is used to planarize the symmetric links in the network. Then, the quick rejection method and straddle experiment are used to remove the cross sections after planarization. Finally, by counting the number of SIL node discharges and facilitating the left-hand rule, PALS successfully reduces the difference between the calculated and actual pest-dense areas. The experiments showed that PALS achieves an average improvement of 15% and a maximum improvement of up to 42.2% across the four experimental settings, indicating its higher accuracy and robustness compared with the state-of-the-art algorithms.
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非对称链接下的太阳能杀虫灯物联网害虫密集区定位方案
太阳能杀虫灯(SIL)与无线传感器网络的结合为农业害虫控制提供了一种绿色高效的解决方案,即太阳能杀虫灯物联网(SIL-IoTs)。在现实的大规模 SIL-IoTs 部署场景中,通过网络收集的综合害虫信息可以有效定位害虫密集区域。然而,无线通信距离不固定、节点放电干扰等各种因素导致的非对称链路问题是 SIL-IoTs 部署的主要障碍。受此问题的启发,本文提出了基于非对称链路的害虫密集区定位策略(PALS)。首先,通过分析 SIL 节点的一跳和两跳信息来判断网络中的非对称节点。然后,使用 Gabriel 图或相对邻域图平面化算法对网络中的对称链路进行平面化处理。然后,使用快速剔除法和跨距实验去除平面化后的横截面。最后,通过计算 SIL 节点的放电次数和促进左手定则,PALS 成功缩小了计算得出的害虫密集区与实际密集区之间的差异。实验结果表明,在四种实验设置中,PALS 平均提高了 15%,最高提高了 42.2%,这表明与最先进的算法相比,PALS 具有更高的准确性和鲁棒性。
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2024 Index IEEE Transactions on AgriFood Electronics Vol. 2 Table of Contents Front Cover IEEE Circuits and Systems Society Information IEEE Circuits and Systems Society Information
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