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LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

机译：Lora通信作为瓦尔松互联网互联网推向农村农场大型牲畜监测的推动者

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Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).
机译：目前，智能农业被认为是提高农场生产力的有效解决方案;因此，它最近从服务提供商提供了广泛的利益，以提供广泛的应用，从害虫识别到资产监测。虽然数字技术的出现，如事物互联网（物联网）和低功率广域网（LPWANS），但在智能农业行业中导致了显着进展，农业运营仍然需要更有效的解决方案。另一方面，许多公民申请领域的许多公民应用领域的利用也称为无人机的无人机（无人机）的利用。本文旨在开发一个农场监测系统，该系统融入了UAV，LPWAN和IOT技术，以改变当前的农场管理方法，并援助农民从农场运营获得可行数据。在这方面，开发了基于物联网的水质监测系统，因为水是畜牧业发展中的重要方面。然后，基于远程广域网（LoraWan®）技术，开发了一种多通道LoraWan®网关并集成到垂直起飞和降落无人机中，以传送来自传感器的收集数据以进一步分析。此外，为了开发基于Lorawan®的空中通信，在不同的配置和场景下进行了一系列测量和模拟。最后，为了提高基于空中的数据收集的效率，优化了UAV路径规划。测量结果表明，通过无人机在空中运行时可实现的LORA覆盖率约为10公里，并且长米不规则地形模型为大规模农场的场景提供了最合适的路径损失模型，以及多重带传播因子为12的通道网关以高的无人机速度（高达95公里/小时）提供最可靠的通信链路。仿真结果表明，发达的系统可以克服Lorawan®的覆盖限制，它可以通过大规模无线传感器网络建立可靠的通信链路。此外，表明，通过优化飞行路径，可以在比工业任务规划（在我们的情况下最多四次）的时间更短的时间内进行空中数据收集。

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