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A Network Architecture for High Volume Data Collection in Agricultural Applications

机译:用于农业应用中的大数据收集的网络架构

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An important requirement for Internet of Things applications is the ability to provide fast and energy efficient data collection from wireless sensors. When sensor nodes are located far from the data collection point, currently available long range protocols present challenges associated with a very low data rate and often unreliable connections resulting in excessive energy consumption related to data transmission. To address this problem, we propose a simple and energy-efficient data collection architecture for smart agricultural purposes which require wireless sensing. The architecture involves data collection from nodes located in remote fields or on animals leveraging off the use of drones as a data collection mechanism. In particular, drones can fly over the desired areas (points) and collect high volumes of data that would be otherwise difficult to transfer directly to the sink in a reasonable amount of time and using reasonable amounts of energy. We describe the different components and stages that constitute the proposed architecture emphasizing the networking component. We propose the use of different communication technologies, such as LoRa and WiFi, depending on the data collection requirements. We present an in-lab development of this architecture as a proof-of-concept as well as preliminary results for the architecture. The results reveal that the proposed solution is potentially capable of achieving data collection at high volume, however, the performance does not consider the highest spreading factors of LoRa.
机译:物联网应用程序的一项重要要求是能够从无线传感器提供快速且节能的数据收集功能。当传感器节点位于远离数据收集点的位置时,当前可用的远程协议提出了与非常低的数据速率和通常不可靠的连接相关的挑战,从而导致与数据传输有关的过多能量消耗。为了解决这个问题,我们提出了一种简单且节能的数据收集架构,用于需要无线传感的智能农业目的。该架构涉及从位于偏远地区或动物身上的节点收集数据,并利用无人机作为数据收集机制。特别是,无人机可以飞越所需区域(点)并收集大量数据,否则这些数据将很难在合理的时间内和使用合理的能量直接传输到接收器。我们描述构成建议的体系结构并强调网络组件的不同组件和阶段。我们建议根据数据收集要求使用不同的通信技术,例如LoRa和WiFi。我们提供此架构的实验室内开发,以作为概念验证以及该架构的初步结果。结果表明,所提出的解决方案有可能能够实现高容量的数据收集,但是,该性能并未考虑LoRa的最高扩展因子。

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