摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-调查人员讨论机器学习的新发现。根据NewsRx记者在澳大利亚默多克的新闻报道，研究表明，“在牧场地区有效利用GPS和移动网络KS进行本地化受到其高功率消耗和高部署成本的限制。远程(LoRa)是一种低功耗的广域网络RK(LPWAN)技术，可以用来缓解这些挑战。”新闻记者从默多克大学的研究中获得了一句话，“与之前的研究不同，目前流行的方法需要多个GAT通道。本研究提出了一种有价值的方法，专注于单个移动LoRa网关进行本地化。采用基于粒子滤波和机器学习的管道，从接收信号强度指示器(RSSI)映射目标节点和网关之间的距离。使用粒子滤波来消除噪声对UCE的影响。”然后，利用支持向量机、随机森林、K近邻等机器学习技术对RSSI值进行距离估计，然后利用质心伪三边法对估计的距离进行跟踪，并在真实世界的半视线环境中进行了测试。利用Lorawan指定的硬件组件和服务器生成的三个数据集，进行了主动搜索和被动监控两个实验，提出了一种迭代估计过程，以解决主动搜索应用所需网关初始位置对精度的影响，结果表明，主动搜索通常需要2-3跳才能到达目标节点。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating in Murdoch, Australia, by NewsRx journalists, research stated, “Effective use of GPS and mobile networ ks for localisation in rangeland areas is constrained by their high power consum ption and high deployment costs. Long-range (LoRa), a low -power wide area netwo rk (LPWAN) technology, can be employed to mitigate these challenges.” The news reporters obtained a quote from the research from Murdoch University, “ In contrast to prior research where the prevalent approaches entail multiple gat eways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning -based pipe line is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to red uce the impact of noise on the RSSI values. Then, several machine learning techn iques, such as support vector machines, random forest, and k -nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudotrilateration method. The propose d method was tested in a real -world semi -line -of -sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two fo rms of experiments were performed: active searching and passive monitoring. We p ropose an iterative estimation process to address the dilution of precision caus ed by the initial positions of the gateway required for active searching applica tions. The results show that active searching typically requires 2 to 3 hops to reach a target node.”
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