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

职业：迈向可扩展、低功耗、广域网

• 批准号: 2142978
• 负责人: Bhuvana Krishnaswamy
• 金额: $ 48.91万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142978&HistoricalAwards=false
• 关键词: CAREER; Towards; Scalable; Low; Power

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.

无线数据传输对于智能农业，牲畜监测以及精确农业等快速增长的领域的实时数据收集和分析至关重要。这样的大规模监控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，因此此类网络仅在有限的位置部署，并且延迟范围从一天到一个月不等。实时数据收集将提供对环境的时空动力学的理解。这样的监视系统通常在大型领域部署传感器，需要较长的电池寿命，较长的通信范围来降低基础设施成本，与更多设备共享媒介以及低成本。但是，他们对沟通速度和延迟的要求较少。现有的解决较长电池寿命和远程通信的策略不会在大型网络中有效共存。允许远距离通信的技术通常会更快地耗尽电池能量，而电池有效的通信技术仅限于短距离。在这项工作中，提出了新颖的沟通算法，协议和架构，以同时实现大规模部署的长距离通信和较长的电池寿命。这项工作的结果将通过实时的土壤健康监测直接影响威斯康星州的农业社区。它将通过社区驱动的无线课程影响高中，本科和研究生教育，这些课程结合了这项工作中开发的原型。拟议的研究采取了以下三个方向来克服可扩展性的障碍，并满足远程，低功率，广阔区域网络（LPWAN）的连通性需求：1）提出了一种新型调制技术，该技术提出了一种新型调制技术，该技术通过在透射之间的时间间隔中在时间间隔中在时间间隔中编码信息来最大程度地减少能源消耗。将在此推力中研究错误校正和源代码以应对正时通道的挑战。 2）在第二个推力中，提出了从碰撞中解码并发传输的干扰取消和分布式干扰。将在此推力中研究现有LPWAN的干扰取消以及避免碰撞的跨层协议。还将探索新的时频分析以解码跨多个网关的数据包碰撞。 3）在第三个推力中，提出了可以接收和解码各种LPWAN技术的统一基于云的LPWAN接收器。将设计以将无线电样品传输到云服务器的压缩算法。基于云的LPWAN接收器使我们能够连贯地结合来自多个网关的信号，进而促进合作解码。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来获得支持的，并具有更广泛的影响。

项目成果

OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework

• 发表时间: 2023
• 作者: Manan Mishra;D. J. Koch;Muhammad Osama Shahid;Bhuvana Krishnaswamy;Krishna Chintalapudi;Suman Banerjee

Towards Practical and Scalable Molecular Networks

• DOI: 10.1145/3603269.3604881
• 发表时间: 2023-09
• 期刊: Proceedings of the ACM SIGCOMM 2023 Conference
• 作者: Jiaming Wang;Sevda Öğüt;Haitham Al Hassanieh;Bhuvana Krishnaswamy

A Novel Time-Interval Based Modulation for Large-Scale, Low-Power, Wide-Area-Networks

• DOI: 10.1145/3549543
• 发表时间: 2022-07
• 期刊: ACM Transactions on Sensor Networks
• 影响因子: 4.1
• 作者: Yaman Sangar;Yoganand Biradavolu;Bhuvana Krishnaswamy

Spreading Factor Detection for Low-Cost Adaptive Data Rate in LoRaWAN Gateways

• DOI: 10.1145/3560905.3568112
• 发表时间: 2022-11
• 期刊: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: D. J. Koch;Muhammad Osama Shahid;Bhuvana Krishnaswamy

Concurrent interference cancellation: decoding multi-packet collisions in LoRa

• DOI: 10.1145/3452296.3472931
• 发表时间: 2021-08
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Muhammad Osama Shahid;Millan Philipose;Krishna Chintalapudi;Suman Banerjee;Bhuvana Krishnaswamy