CAREER: Advancing Network Configuration and Runtime Adaptation Methods for Industrial Wireless Sensor-Actuator Networks

十年来工业无线标准的实际部署，如WirelessHART和ISA100，已经证明了使用基于IEEE 802.15.4的无线传感器-执行器网络（wsan）在工业环境中实现可靠和实时无线通信的可行性。尽管经过多年的研究，WSAN在大多数情况下都能令人满意地工作，但由于配置WSAN是一个复杂的过程，涉及理论计算、仿真、现场测试等任务，因此配置WSAN往往很困难。为了支持需要高数据速率和移动平台的新业务，工业无线局域网正在采用5G和LoRa等无线技术，并日益分层、异构和复杂，这大大增加了网络配置的难度。该CAREER项目旨在推进工业无线局域网的网络配置和运行时适应方法。该项目的研究成果将显著提高工业无线局域网的弹性和敏捷性，减少人工参与网络管理，从而显著提高工业效率，显著降低运营成本。通过提供更先进的无线局域网，该项目的研究成果将极大地促进过程工业中无线局域网的安装，并实现广泛的基于无线的新应用，从而影响经济、安全和生活质量。该项目改进了讲座和课程项目材料，支持了课程开发，为本科生和研究生创造了研究机会，并为K-12学生建立了拓展项目。传统方法在很大程度上依赖于经验和经验法则，包括在一些现场试验中对网络负载或动态进行粗粒度分析，与此不同，该项目开发了一种严格的方法，利用先进的机器学习技术，通过收集无线研究界积累的宝贵资源（例如，理论模型和仿真方法）来配置和适应wsan。该项目开发了新的方法，利用无线模拟和深度学习将高级网络性能与低级网络配置联系起来，并在运行时有效地调整网络，以满足工业应用指定的性能要求。本项目通过测试平台实验、案例研究和实际验证，展示了配备这些新方法的wsan的性能。该项目的研究成果不仅影响工业无线局域网，还影响其他复杂的无线网络，因为该项目为新的网络配置和运行时适应策略创建了一个可复制的模板，从而推动了无线网络管理的发展。该奖项反映了美国国家科学基金会的法定使命，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。

A decade of real-world deployments of industrial wireless standards, such as WirelessHART and ISA100, has demonstrated the feasibility of using IEEE 802.15.4-based wireless sensor-actuator networks (WSANs) to achieve reliable and real-time wireless communication in industrial environments. Although WSANs work satisfactorily most of the time thanks to years of research, they are often difficult to configure as configuring a WSAN is a complex process, which involves theoretical computation, simulation, field testing, among other tasks. To support new services that require high data rates and mobile platforms, industrial WSANs are adopting wireless technologies such as 5G and LoRa and becoming increasingly hierarchical, heterogeneous, and complex, which significantly increases the network configuration difficulty. This CAREER project aims to advance network configuration and runtime adaptation methods for industrial WSANs. Research outcomes from this project will significantly enhance the resilience and agility of industrial WSANs and reduce human involvement in network management, leading to a significant improvement in industrial efficiency and a remarkable reduction of operating costs. By providing more advanced WSANs, the research outcomes from this project will significantly spur the installation of WSANs in process industries and enable a broad range of new wireless-based applications, which affects economics, security, and quality of life. This project enhances lectures and course project materials, supports curriculum developments, creates research opportunities for undergraduate and graduate students, and establishes outreach programs for K-12 students. Different from traditional methods that rely largely on experience and rules of thumb that involve a coarse-grained analysis of network load or dynamics during a few field trials, this project develops a rigorous methodology that leverages advanced machine learning techniques to configure and adapt WSANs by harvesting the valuable resources (e.g., theoretical models and simulation methods) accumulated by the wireless research community. This project develops new methods that leverage wireless simulations and deep learning to relate high-level network performance to low-level network configurations and efficiently adapt the network at runtime to satisfy the performance requirements specified by industrial applications. This project demonstrates the performance of WSANs that are equipped with those new methods through testbed experimentation, case study, and real-world validation. The research outcomes from this project affects not only industrial WSANs but other complex wireless networks as this project creates a replicable template for novel network configuration and runtime adaptation strategies that advance the state of the art of wireless network management.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.