CAREER: Protocols for Low-Power Wide-Area Networks in White Spaces

职业：空白区域低功耗广域网协议

• 批准号: 1846126
• 负责人: Abusayeed Saifullah
• 金额: $ 55.05万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846126&HistoricalAwards=false
• 关键词: CAREER; Protocols; Low; Power; Wide

This project will integrate research and education in the design and implementation of a Low-Power Wide-Area Network (LPWAN) in the TV White Spaces. LPWANs enable low-power devices to transmit over long distances for various Internet-of-Things (IoT) applications. Today, LPWANs have a number of major limitations making their adoption challenging. First, they rely on wired infrastructure for integrating multiple networks to cover very large areas (e.g., smart city). Lack of proper infrastructure hinders their applicability to rural/remote wide-area applications such as agricultural (e.g., smart farming) and industrial IoT (e.g., oil-field monitoring). Second, rapid growth of LPWANs in the limited spectrum raises the challenge of coexistence. Third, current (non-cellular) LPWANs are not designed to effectively support mobile nodes, e.g., tractors and drones in smart farming. Finally, LPWANs are not yet designed to support real-time communication hindering their adoption for many applications (e.g., process control). This project will address these challenges by developing theoretical foundations and systems for SNOW (Sensor Network Over White Spaces), an LPWAN that exploits unused TV spectrum, called white spaces. The results are extendable to many other LPWANs. Due to abundant white spaces, this project will enable connectivity for many rural applications. It will integrate education through course development, student research, and outreach to minority/underrepresented and K-12 students. This project will design and implement an LPWAN architecture and complete protocol stack based on SNOW to support scalable integration, coexistence, mobility, and real-time communication as follows. (1) It proposes a scalable seamless integration of multiple SNOWs by minimizing latency in the integrated network. Considering tradeoffs between quality and execution time, the solution approach includes both global optimization and fast heuristics. (2) It proposes a novel approach based on Reinforcement Learning to handle coexistence with many independent networks. This is done by developing an efficient Q-learning framework that is practical at low-power nodes. This will be the first Q-learning approach for LPWAN and for handling coexistence in a low-power network. (3) It proposes lightweight cross-layer approaches to enable mobility of SNOW nodes by handling Doppler effect as well as the effects of geospatial variation of white spaces. (4) It proposes a real-time communication framework for SNOW which will be the first result on real-time scheduling for LPWAN. (5) It will implement the proposed protocols on TI CC1310 and also on universal software radio peripheral devices. The protocols will be evaluated through experiments in two different radio environments -- an urban test-bed and an agricultural field piloting smart farming.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.

该项目将把研究和教育整合到电视空白区低功耗广域网 (LPWAN) 的设计和实施中。 LPWAN 使低功耗设备能够为各种物联网 (IoT) 应用进行长距离传输。如今，LPWAN 存在许多主要限制，使其采用具有挑战性。首先，它们依靠有线基础设施来集成多个网络以覆盖非常大的区域（例如智慧城市）。缺乏适当的基础设施阻碍了它们在农村/偏远广域应用中的适用性，例如农业（例如智能农业）和工业物联网（例如油田监控）。其次，LPWAN 在有限频谱内的快速增长提出了共存的挑战。第三，当前（非蜂窝）LPWAN 的设计不能有效支持移动节点，例如智能农业中的拖拉机和无人机。最后，LPWAN 尚未设计为支持实时通信，这阻碍了其在许多应用程序（例如过程控制）中的采用。该项目将通过开发 SNOW（空白空间传感器网络）的理论基础和系统来应对这些挑战，SNOW 是一种利用未使用的电视频谱（称为空白空间）的 LPWAN。结果可扩展到许多其他 LPWAN。由于拥有丰富的空白空间，该项目将为许多农村应用提供连接。它将通过课程开发、学生研究以及对少数族裔/弱势群体和 K-12 学生的宣传来整合教育。该项目将设计和实现基于 SNOW 的 LPWAN 架构和完整协议栈，以支持可扩展集成、共存、移动性和实时通信，如下所示。 (1) 它提出了通过最小化集成网络中的延迟来实现多个 SNOW 的可扩展无缝集成。考虑到质量和执行时间之间的权衡，解决方案方法包括全局优化和快速启发式。 (2)提出了一种基于强化学习的新方法来处理与许多独立网络的共存。这是通过开发适用于低功耗节点的高效 Q 学习框架来实现的。这将是第一个用于 LPWAN 和处理低功耗网络共存的 Q 学习方法。 (3) 提出了轻量级跨层方法，通过处理多普勒效应以及空白地理空间变化的影响来实现 SNOW 节点的移动性。 (4)提出了SNOW的实时通信框架，这将是LPWAN实时调度的第一个成果。 (5) 它将在 TI CC1310 以及通用软件无线电外围设备上实现所提议的协议。这些协议将通过在两个不同的无线电环境中进行实验进行评估——城市试验台和试点智能农业的农田。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Long-Lived LoRa: Prolonging the Lifetime of a LoRa Network

长寿命 LoRa：延长 LoRa 网络的使用寿命

• DOI: 10.1109/icnp49622.2020.9259375
• 发表时间: 2020-08-20
• 期刊: 2020 IEEE 28th International Conference on Network Protocols (ICNP)
• 作者: Sezana Fahmida;V. P. Modekurthy;Mahbubur Rahman;Abusayeed Saifullah;Marco Brocanelli
• 通讯作者: Marco Brocanelli

LPWAN in the TV White Spaces: A Practical Implementation and Deployment Experiences

电视空白区中的 LPWAN：实际实施和部署经验

• DOI: 10.1145/3447877
• 发表时间: 2021-06
• 期刊: ACM transactions on embedded computing systems
• 作者: Rahman, Mahbubur;Ismail, Dali;Modekurthy, Venkata P.;Saifullah, Abusayeed
• 通讯作者: Saifullah, Abusayeed

A Distributed Real-time Scheduling System for Industrial Wireless Networks

工业无线网络分布式实时调度系统

• DOI: 10.1145/3464429
• 发表时间: 2021-07-01
• 期刊: ACM Transactions on Embedded Computing Systems (TECS)
• 作者: V. P. Modekurthy;Abusayeed Saifullah;S. Madria
• 通讯作者: S. Madria

Mobility in Low-Power Wide-Area Network over White Spaces

空白空间低功耗广域网的移动性

• DOI: 10.5555/3451271.3451283
• 发表时间: 2024-09-13
• 期刊: Earth-Science Reviews
• 影响因子: 12.1
• 作者: Abusayeed Saifullah;Mahbubur Rahman;Dali Ismail;Chenyang Lu;Jie Liu;Ranveer Ch;ra;ra
• 通讯作者: ra

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

集成低功耗广域网以增强可扩展性和扩大覆盖范围

• DOI: 10.1109/tnet.2020.2963886
• 发表时间: 2020-01-01
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Mahbubur Rahman;Abusayeed Saifullah
• 通讯作者: Abusayeed Saifullah