Emerging autonomous farming techniques rely on smart devices such as multi-spectral cameras, collecting fine-grained data, and robots performing tasks such as de-weeding, berry-picking, etc. These techniques require a high throughput network, supporting 10s of Mbps per device at the scale of tens to hundreds of devices in a large farm. We conduct a survey across 12 agronomists to understand these networking requirements of farm workloads and perform extensive measurements of WiFi 6 performance in a farm to identify the challenges in meeting them. Our measurements reveal how network capacity is fundamentally limited in such a setting, with severe degradation in network performance due to crop canopy, and spotlight farm networks as an emerging new problem domain that can benefit from smarter network resource management decisions. To that end, we design Cornet, a network for supporting on-farm applications that comprises: (i) a multi-hop mesh of WiFi routers that uses a strategic combination of 2.4GHz and 5GHz bands as informed by our measurements, and (ii) a centralized traffic engineering (TE) system that uses a novel abstraction of resource units to reason about wireless network capacity and make TE decisions (schedule flows, assign flow rates, and select routes and channels). Our evaluation, using testbeds in a farm and trace-driven simulations, shows how Cornet achieves 1.4 $\times$ higher network utilization and better meets application demands, compared to standard wireless mesh strategies.

翻译：新兴的自主农业技术依赖于智能设备（如多光谱相机）、收集细粒度数据的设备以及执行除草、采摘等任务的机器人。这些技术需要高吞吐量网络，在大型农场中支持数十台至数百台设备，每台设备需要数十Mbps的带宽。我们对12名农学家进行了调研，以了解农场工作负载的网络需求，并在农场中对WiFi 6性能进行了广泛测量，以识别满足这些需求面临的挑战。测量结果表明，在这种环境下，网络容量从根本上受到限制，作物冠层导致网络性能严重下降，并突出表明农场网络是一个新兴的问题领域，可从更智能的网络资源管理决策中受益。为此，我们设计了Cornet，一种支持农场内应用的网络，包括：(i) 一个由WiFi路由器组成的多跳网格，根据测量结果战略性地结合2.4GHz和5GHz频段；(ii) 一个集中式流量工程（TE）系统，该系统使用新颖的资源单元抽象来推理无线网络容量并做出TE决策（调度流、分配流速率、选择路由和信道）。我们在农场使用测试平台和基于轨迹的模拟进行的评估表明，与标准无线网格策略相比，Cornet实现了1.4倍的网络利用率提升，并更好地满足了应用需求。