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首页> 外文期刊>International journal of communication systems >Geolocation analysis for Search And Rescue systems using LoRaWAN
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Geolocation analysis for Search And Rescue systems using LoRaWAN

机译:利用洛拉瓦的搜索和救援系统地理位置分析

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Low-power wide-area network (LPWAN) technologies are aiming to provide power-efficient solutions to the field of Internet of Things (IoT). Over the last years, we have seen a significant development within the area of IoT applications. For many applications, the problem of localization (i.e., determine the physical location of nodes) is critical. An area study of such use case is also the rescue monitor systems. In this study, we start by describing a solution designed for the long range wide area network (LoRaWAN) to localize position of IoT modules such as wearables used from vulnerable groups. Through performance study of the behavior of a LoRaWAN channel and using trilateration and RSSI information, the localization of an IoT wearable can be acquired within a small range. Routing people in need is one of the use cases the above mechanism could be integrated so as to be able to be tracked by familiar people. After that, we evaluate the usage of mathematical model of multilateration algorithms using time difference of arrival (TDoA) as a solution for positioning over LoRaWAN. The research is carried out using simulations in Python by configuring the constant positions of the Gateways inside an outdoor area. The proposed algorithms can be integrated in application for tracking people at any time and especially routing people from vulnerable groups. Through multilateration and algorithm's prediction, we can have an accuracy of 40-60 m in location positioning, ideal for search and rescue use cases. We finally summarize the above algorithms' estimation and general behavior in a SAR system.
机译:低功耗广域网(LPWAN)技术旨在为事物互联网(物联网)提供高效的解决方案。在过去几年中,我们在IoT应用领域看到了一个重要的发展。对于许多应用,本地化问题(即,确定节点的物理位置)至关重要。这种用例的区域研究也是救援监控系统。在本研究中,我们首先描述为长距离广域网(LoraWan)设计的解决方案,以定位IOT模块的位置,例如来自易受攻击的组使用的可穿戴物品。通过对LoraWan通道的行为和使用三边和RSSI信息的性能研究,可以在一个小范围内获得物联网可穿戴的本地化。需要的路由人员是使用情况之一,可以集成上述机制,以便能够被熟悉的人跟踪。之后,我们使用时间差来评估多边算法的数学模型(TDOA)作为定位Lorawan的解决方案。通过在户外区域内的网关的恒定位置配置Python中使用Python进行研究。所提出的算法可以集成在申请中,以便随时跟踪人,尤其是从弱势群体中路由人员。通过多管和算法的预测,我们可以在位置定位的精度为40-60米,非常适合搜索和救援用例。我们终于总结了SAR系统中的上述算法估计和一般行为。

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