Performance Evaluation of LoRa Networks in an Open Field Cultivation Scenario

Aikaterini Griva, A. Boursianis, Shaohua Wan, P. Sarigiannidis, G. Karagiannidis, S. Goudos
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引用次数: 3

Abstract

The employment of Internet of Things (IoT) technology in agriculture could be beneficial in managing the cultivation production in a highly-customizable way. LoRa (Long Range) is one of the most important technologies in cultivation fields mainly thanks to its ability to provide long-range transmission and low power consumption. In this paper, we evaluate the performance of LoRa networks in an open field cultivation scenario via simulations using FLoRa, an open-source framework in OMNeT++. The number of nodes, the number of gateways, the antenna gain, and the size of the deployment area have a considerable impact on both the data extraction rate and the energy consumption of a LoRa network. Our results show that the optimization of the parameters that affect the performance of a LoRa network could transform traditional agriculture into a new perspective of smart cultivation. Finally, we evaluate the impact of the density and the geometric characteristics of three types of crop (tomatoes, grapes, apples) on the number of intersections that were caused by the insertion of physical objects-obstacles in a LoRa network.
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开放式耕作场景下LoRa网络的性能评价
物联网(IoT)技术在农业中的应用有助于以高度可定制的方式管理种植生产。LoRa (Long Range)技术具有远距离传输和低功耗的特点,是目前农业领域最重要的技术之一。在本文中,我们通过使用omnet++中的开源框架FLoRa进行模拟,评估了LoRa网络在开放田间种植场景下的性能。节点数量、网关数量、天线增益和部署区域的大小对LoRa网络的数据提取速率和能耗都有相当大的影响。我们的研究结果表明,优化影响LoRa网络性能的参数可以将传统农业转变为智能种植的新视角。最后,我们评估了三种作物(西红柿、葡萄、苹果)的密度和几何特征对LoRa网络中物理物体障碍物插入造成的交叉点数量的影响。
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