CAREER: Towards Scalable, Low-Power, Wide Area Networks

无线数据传输对于智能农业、牲畜监测和精准农业等快速增长领域的实时数据收集和分析至关重要。这种大规模监测网络目前使用蜂窝和卫星网络收集数据。由于太阳能电池板和电池的基础设施成本很高，这种网络只部署在有限的地点，延迟时间从一天到一个月不等。实时数据收集将有助于了解环境的时空动态。这种监控系统通常在大的领域部署传感器，并且需要长的电池寿命、长的通信范围以降低基础设施成本、与更多的设备共享介质以及低成本。然而，它们对通信速度和延迟的要求不太严格。解决长电池寿命和长距离通信的现有策略在大规模网络中不能有效地共存。允许长距离通信的技术通常会更快地消耗电池能量，而电池效率高的通信技术仅限于短距离。在这项工作中，提出了新的通信算法、协议和架构，以在大规模部署中同时实现远程通信和长电池寿命。这项工作的成果将通过实时土壤健康监测对威斯康星州农业社区产生直接影响。它将影响高中，本科和研究生教育，通过社区驱动的无线课程，结合在这项工作中开发的原型。为了克服可扩展性的障碍并满足长距离、低功耗、广域网（LPWAN）的连接需求，所提出的研究采取以下三个方向：1）提出了一种新的调制技术，其通过在传输之间的时间间隔中编码信息来最小化能量消耗。本课程将研究解决定时信道挑战的纠错和源代码。2)在第二个推力，干扰消除和分布式干扰避免解码冲突的并发传输。在现有的LPWAN（如LoRa）中的干扰消除，以及避免冲突的跨层协议，将在本研究中进行研究。新的时间-频率分析解码跨多个网关的数据包冲突也将进行探讨。3)在第三个推力中，提出了一种统一的基于云的LPWAN接收器，可以接收和解调各种LPWAN技术。将设计用于将无线电样本传输到云服务器的压缩算法。基于云的LPWAN接收器使我们能够相干地组合来自多个网关的联合收割机信号，从而促进合作解码。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Wireless data delivery is crucial for real-time data collection and analysis in the fast-growing areas of smart agriculture, livestock monitoring, and precision farming among others. Such large-scale monitoring networks currently use cellular and satellite networks to collect data. Due to the high infrastructure costs for solar panels and batteries, such networks are deployed only at limited locations and have delays ranging from a day up to a month. Real-time data collection will provide an understanding of the spatio-temporal dynamics of an environment. Such monitoring systems typically deploy sensors in large fields and require long battery life, long communication range to reduce infrastructure costs, share the medium with many more devices, and low cost. However, they have less stringent demands on communication speed and delay. Existing strategies for addressing long battery life and long-range communication do not co-exist efficiently in a large-scale network. Technologies that allow long-range communication typically drain the battery energy faster, while battery-efficient communication technologies are limited to short distances. In this work, novel communication algorithms, protocols, and architectures are proposed to simultaneously achieve long-range communication and long battery life in large-scale deployments. The outcomes of this work will have a direct impact on the Wisconsin farming community through real-time soil health monitoring. It will impact high-school, undergraduate, and graduate education through community-driven wireless courses incorporating prototypes developed in this work. The proposed research takes the following three directions to overcome the barriers for scalability and meet the connectivity needs of long-range, low-power, wide-area networks (LPWAN): 1) A novel modulation technique that minimizes energy consumption by encoding information in the time interval between transmissions is proposed. Error correction and source codes to address the challenges of a timing channel will be studied in this thrust. 2) In the second thrust, interference cancellation and distributed interference avoidance to decode concurrent transmissions from collisions are proposed. Interference cancellation in existing LPWANs such as LoRa, as well as cross-layer protocols to avoid collisions, will be studied in this thrust. Novel time-frequency analysis to decode packet collisions across multiple gateways will also be explored. 3) In the third thrust, a unified cloud-based LPWAN receiver that can receive and demodulate a variety of LPWAN technologies is proposed. Compression algorithms to transmit radio samples to a cloud server will be designed. A cloud-based LPWAN receiver enables us to coherently combine signals from multiple gateways, in turn facilitating cooperative decoding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.