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.

该项目将在电视白色空间的低功耗广域网（LPWAN）的设计和实施中整合研究和教育。LPWAN使低功耗设备能够在各种物联网（IoT）应用中进行长距离传输。今天，LPWAN有许多主要的限制，使其采用具有挑战性。首先，它们依赖于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适用于农村/偏远的广域应用，如农业（例如，智能农业）和工业物联网（例如，油田监测）。其次，有限频谱中LPWAN的快速增长提出了共存的挑战。第三，当前（非蜂窝）LPWAN未被设计成有效地支持移动的节点，例如，智能农业中的拖拉机和无人机。最后，LPWAN尚未被设计为支持实时通信，这阻碍了它们被许多应用采用（例如，过程控制）。该项目将通过为SNOW（白色空间上的传感器网络）开发理论基础和系统来解决这些挑战，SNOW是一种利用未使用的电视频谱（称为白色空间）的LPWAN。结果可推广到许多其他的LPWAN。由于丰富的白色空间，该项目将使许多农村应用的连接。它将通过课程开发，学生研究和推广到少数民族/代表性不足和K-12学生整合教育。本项目将设计和实现一个LPWAN架构和完整的基于SNOW的协议栈，以支持可扩展的集成，共存，移动性和实时通信如下。(1)它提出了一个可扩展的无缝集成的多个SNOW，最大限度地减少延迟集成网络。考虑到质量和执行时间之间的权衡，解决方案的方法包括全局优化和快速算法。(2)它提出了一种基于强化学习的新方法来处理多个独立网络的共存问题。这是通过开发一个有效的Q学习框架来实现的，该框架在低功耗节点上是实用的。这将是LPWAN的第一个Q学习方法，用于处理低功耗网络中的共存。(3)它提出了轻量级的跨层方法，使移动性的雪节点处理多普勒效应以及地理空间变化的影响，白色空间。(4)它提出了一个实时通信框架SNOW，这将是第一个实时调度LPWAN的结果。(5)它将在TI CC 1310和通用软件无线电外围设备上实现所提出的协议。该协议将通过在两种不同无线电环境中的实验进行评估-城市测试台和智能农业试点农田。该奖项反映了NSF的法定使命，并被认为值得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。

项目成果

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

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

A Distributed Real-time Scheduling System for Industrial Wireless Networks

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

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

• DOI: 10.5555/3451271.3451283
• 发表时间: 2021
• 作者: Abusayeed Saifullah;Mahbubur Rahman;Dali Ismail;Chenyang Lu;Jie Liu;Ranveer Chandra