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

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了解，以显著扩大覆盖范围和稳定性，加速物联网(IoT)在全球的采用。传感器系统的这些改进寻求使一系列应用受益，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。覆盖范围和成本优势对农村或经济困难地区的美国人口尤其有影响，这些地区缺乏经济高效的连接，无法利用物联网优势。此外，该项目将加强产业界和学术界的合作伙伴关系，实现创新的技术转型，并扩大女性在科学、技术、教育和数学(STEM)领域的参与。该小型企业创新研究(SBIR)第一阶段项目旨在通过使用LPWA物联网(LIMA)设备以经济高效和易于部署的方式增强终端节点和网关之间的连接，从而实现可扩展、更持久和更高吞吐量的低功率广域(LPWA)物联网(IoT)网络。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。这两个团队寻求增强LoRaWAN(远程广域网络的标准)的连接性，目标是将覆盖范围扩大几倍，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LPWAN特有的超低比特率，并可在LPWAN技术的各种应用中推广。LIMA解决方案建立在两个协同创新的基础上：(A)嵌入式控制路由，它使用数据分组报头中的极少量比特来代替控制分组，从而在超低容量、能量受限的网络中实现可扩展性和能效；以及(B)基于关系型深度强化学习的路由，它将强化学习和深度神经网络(DeepRL)与使用关系特征相结合，以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终端节点。预计的技术成果包括网状网络软件、模拟模型、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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.