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发展领域的实时数据收集和分析至关重要。这种大规模的监测网络目前使用蜂窝和卫星网络来收集数据。由于太阳能电池板和电池的基础设施成本高昂，这种网络只能在有限的地点部署，并且延迟从一天到一个月不等。实时数据收集将提供对环境时空动态的理解。这种监测系统通常在大范围内部署传感器，需要较长的电池寿命，较长的通信范围以降低基础设施成本，与更多设备共享介质，并且成本低。然而，它们对通信速度和延迟的要求不那么严格。现有的解决长电池寿命和远程通信的策略不能在大规模网络中有效地共存。允许远程通信的技术通常会更快地耗尽电池能量，而电池高效通信技术则仅限于短距离。在这项工作中，提出了新的通信算法，协议和架构，以同时实现大规模部署中的远程通信和长电池寿命。这项工作的结果将通过实时土壤健康监测对威斯康星州的农业社区产生直接影响。它将通过社区驱动的无线课程，结合这项工作开发的原型，影响高中、本科和研究生教育。为了克服可扩展性的障碍，满足远程、低功耗、广域网（LPWAN）的连接需求，本文从以下三个方面进行了研究：1)提出了一种新的调制技术，通过在传输间隔的时间内对信息进行编码，使能量消耗最小化。错误纠正和源代码，以解决时序信道的挑战将在本推力研究。2)在第二次推力中，提出了干扰消除和分布式干扰避免对碰撞并发传输进行解码。本文将研究现有lpwan（如LoRa）中的干扰消除以及避免碰撞的跨层协议。新的时频分析解码数据包冲突跨多个网关也将探讨。3)提出了一种统一的基于云的LPWAN接收器，可以接收和解调多种LPWAN技术。将设计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信号，从而促进协作解码。该奖项反映了美国国家科学基金会的法定使命，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.