SBIR Phase I: Scalable Mesh Routing to Augment Low-Power Wide Area Internet of Things (IoT) Networks

SBIR 第一阶段：可扩展网状路由以增强低功耗广域物联网 (IoT) 网络

• 批准号: 2136427
• 负责人: Ram Ramanathan
• 金额: $ 22.97万
• 依托单位: GOTENNA, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136427&HistoricalAwards=false
• 关键词: SBIR; Phase; Scalable; Mesh; Routing

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project seeks to enhance the understanding of wireless mesh networks and deep reinforcement learning algorithms in order to significantly expand the coverage and robustness, accelerating Internet of Things (IoT) adoption across the globe. These improvements to sensor systems seek to benefit a range of applications including public safety, smart agriculture, supply chain logistics, smart cities, wildlife monitoring, healthcare and other markets. The coverage and cost benefits are especially impactful for the U.S population in rural or economically-disadvantaged areas that lack cost effective connectivity and are unable to take advantage of the IoT benefits. Further, the project will enhance industry-academia partnership, enable the technology transition of innovations, and expand the participation of women in science, technology, enducation and mathematics (STEM).This Small Business Innovation Research (SBIR) Phase I project seeks to enable scalable, longer-lasting, and higher-throughput Low Power Wide Area (LPWA) Internet of Things (IoT) networks by using LPWA IoT Mesh Augmentation (LIMA) devices to augment the connectivity between end-nodes and gateways in a cost-effective and easy-to-deploy manner. The mesh network of LIMA devices will adaptively multi-hop relay messages to maximize effective capacity and range. The teams seeks to augment the connectivity of LoRaWAN (a standard for Long Range Wide Area Networks) with a goal of increasing coverage range several-fold, reducing the battery drain of end-nodes, and enabling higher uplink bitrates. The core challenge for such LIMA is the development of a scalable multi-hop mesh routing protocol that, unlike existing protocols, accommodates the ultra-low bit rates that characterize LPWANs, and is generalizable across the diverse applications of LPWAN technology. The LIMA solution builds upon two synergistic innovations: (a) embedded-control routing, that uses a very small amount of bits in the data packet header in lieu of control packets, and thereby achieves scalability and energy-efficiency in ultra-low-capacity, energy-constrained networks and (b) relational deep reinforcement learning based routing that combines Reinforcement Learning and Deep Neural Networks (DeepRL) with the use of relational features to learn routing policies that adapt to a variety of link dynamics and traffic conditions. The LIMA solution does not require changes to end-nodes. The anticipated technical deliverables include mesh networking software, a simulation model, a 4-node prototype using a LoRa integrated circuit, and experimental analysis.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.

该小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力旨在增强对无线网状网络和深度强化学习算法的理解，以显着扩大覆盖范围和鲁棒性，加速物联网（IoT）在地球仪的采用。传感器系统的这些改进旨在使一系列应用受益，包括公共安全、智能农业、供应链物流、智能城市、野生动物监测、医疗保健和其他市场。覆盖范围和成本效益对农村或经济落后地区的美国人口特别有影响，这些地区缺乏具有成本效益的连接，无法利用物联网的好处。此外，该项目将加强产学合作伙伴关系，实现创新的技术转型，并扩大妇女在科学，技术，教育和数学（STEM）中的参与。和更高吞吐量的低功耗广域（LPWA）物联网（IoT）网络，通过使用LPWA物联网网状增强（LIMA）设备来增强端与端之间的连接节点和网关的成本效益和易于部署的方式。LIMA设备的网状网络将自适应地多跳中继消息以最大化有效容量和范围。该团队试图增强LoRaWAN（远程广域网标准）的连接性，目标是将覆盖范围增加数倍，减少终端节点的电池消耗，并实现更高的上行链路比特率。这种LIMA的核心挑战是开发一种可扩展的多跳网状路由协议，与现有协议不同，该协议适应表征LPWAN的超低比特率，并且可在LPWAN技术的各种应用中推广。LIMA解决方案基于两项协同创新：（a）嵌入式控制路由，其在数据分组报头中使用非常少量的比特来代替控制分组，从而在超低容量中实现可伸缩性和能量效率，能量约束网络和（B）基于关系深度强化学习的路由，其结合了强化学习和深度神经网络（DeepRL）使用关系特征来学习适应各种链路动态和流量条件的路由策略。LIMA解决方案不需要更改端节点。预期的技术成果包括网状网络软件、仿真模型、使用LoRa集成电路的4节点原型和实验分析。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。