CAREER: A Networking and Learning Co-Design Framework for Data-Efficient Resource Management

职业：用于数据高效资源管理的网络和学习协同设计框架

• 批准号: 2239458
• 负责人: Wan Du
• 金额: $ 56万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239458&HistoricalAwards=false
• 关键词: CAREER; Networking; Learning; Co; Design

Modern buildings require complex management of heating, ventilation, and air-conditioning (HVAC), lighting, blinds, and windows through collection of data from a variety of sensors. LoRaWAN (an open LPWAN protocol) along with LoRa physical layer technology (long range) has been a good choice for large-scale sensor networking with ability to offer long communication distance at low cost. According to LoRa Alliance (which manages the development of LoRaWAN), LoRaWAN outperforms conventional wireless networks (e.g., ZigBee and Wi-Fi) for smart buildings in many aspects, such as easy coverage of several floors in a building with a simple gateway, low power consumption, and long battery life of up to ten years. However, the current version of LoRaWAN can benefit from improved high transmission accuracies and lower energy consumption of sensor nodes. To tackle the above limitations of current LPWAN and machine learning solutions used for in-building climate control system, this project aims to investigate holistic networking and learning framework for resource management. In particular, the project focuses on building energy management as an example application. The goal is to design a building energy management system that optimizes the energy consumption of in-building climate systems jointly while meeting requirements of human comfort. The proposed building energy management system aims at achieving three design goals: 1) maximizing energy saving while maintaining occupants’ comfort, 2) being able to be deployed in buildings of multiple systems, and 3) searching for the optimal control policy with data efficiency. The proposed research activities will be carefully integrated with education activities at UC Merced, a Hispanic-serving institution, including curriculum development, interdisciplinary education, and engaging underrepresented groups.This project will develop a holistic networking and learning framework to jointly control climate in building. Three design goals will be achieved: maximizing energy saving while maintaining occupants’ comfort, being readily deployable in buildings with multiple control systems, and searching for the optimal control policy with data efficiency. TO achieve these design goals, the project is organized into three research thrusts: 1) building an indoor low-power wide area networking system for reliable data collection by a novel rateless-enabled data transmission mechanism and a bit-level network resource allocation scheme; 2) developing a data-efficient model-based reinforcement learning system for resource management by tackling fundamental research problems, such as incomplete training data, representation learning for mitigating data noise, and the bias problem of system dynamics models; 3) designing a networking and learning co-design scheme that considers a set of optimization goals in a unified framework, including building energy saving, occupants’ comfort, data efficiency of reinforcement learning, and wireless network lifetime.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.

现代建筑需要通过从各种传感器收集数据来对供暖、通风和空调（HVAC）、照明、百叶窗和窗户进行复杂的管理。LoRaWAN（一种开放的LPWAN协议）沿着LoRa物理层技术（远程）一直是大规模传感器网络的良好选择，能够以低成本提供长距离通信。根据LoRa联盟（负责管理LoRaWAN的开发）的说法，LoRaWAN优于传统的无线网络（例如，ZigBee和Wi-Fi）在许多方面适用于智能建筑，例如通过简单的网关轻松覆盖建筑物中的几个楼层，低功耗以及长达十年的电池寿命。然而，当前版本的LoRaWAN可以受益于提高的高传输精度和降低传感器节点的能耗。为了解决目前用于建筑物内气候控制系统的LPWAN和机器学习解决方案的上述局限性，该项目旨在研究用于资源管理的整体网络和学习框架。特别是，该项目侧重于建筑能源管理作为一个示例应用程序。目的是设计一个建筑能源管理系统，在满足人类舒适性要求的同时，优化建筑内气候系统的能源消耗。所提出的建筑物能源管理系统旨在实现三个设计目标：1）在保持居住者舒适度的同时最大限度地节省能源，2）能够部署在多个系统的建筑物中，以及3）搜索具有数据效率的最优控制策略。拟议的研究活动将与加州大学默塞德的教育活动仔细整合，这是一所西班牙裔服务机构，包括课程开发，跨学科教育和参与代表性不足的群体。该项目将开发一个整体的网络和学习框架，以共同控制建筑气候。三个设计目标将实现：最大限度地节省能源，同时保持居住者的舒适度，易于部署在建筑物中的多个控制系统，并寻找最佳的控制策略与数据效率。为了实现这些设计目标，本项目分为三个研究方向：1）通过一种新的无速率数据传输机制和比特级网络资源分配方案，构建一个室内低功耗的广域网络系统，以实现可靠的数据采集; 2）通过解决基础研究问题，如不完整的训练数据，开发一个用于资源管理的数据高效的基于模型的强化学习系统，用于减轻数据噪声的表示学习，以及系统动态模型的偏差问题; 3）设计一种网络和学习协同设计方案，该方案在统一的框架中考虑一组优化目标，包括建筑节能，居住者舒适度，强化学习的数据效率，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评估，被认为值得支持。影响审查标准。