CRII: NeTS: Self-Adaptation in Industrial Wireless Sensor-Actuator Networks

CRII：NeTS：工业无线传感器执行器网络的自适应

• 批准号: 1657275
• 负责人: Mo Sha
• 金额: $ 17.5万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657275&HistoricalAwards=false
• 关键词: CRII; NeTS; Self; Adaptation; Industrial

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as steel mills, oil refineries, and chemical plants. Recent years have witnessed an increased interest in adopting wireless sensor-actuator network (WSAN) technology for industrial networks. This project will develop highly self-adaptive WSANs, enabling a broad range of industrial process applications, which affect economics, security, and quality of life. Successful completion of this project will significantly spur the installation of WSANs with the potential of greatly improving industrial efficiency, leading to a significant reduction of the operating costs, which can help create more jobs. The end objective of this project is to incorporate the project outcomes into the next generation of industrial WSAN standards and real-world products. Project findings will be presented at major international conferences and published in their proceedings and high-impact journals and also used for enriching education and outreach.IEEE 802.15.4 based WSANs operate at low-power and can be manufactured inexpensively, which makes them ideal for industrial process applications where energy consumption and costs are important. However, the stringent and diverse quality of service (QoS) requirements and dynamic industrial environments make managing WSANs a daunting task. A key missing piece of the WSAN management puzzle is a self-adaptation component, which allows WSANs to adapt themselves to consistently satisfy the dynamic QoS requirements in uncertain environments. Industry consequently has shown a marked reluctance to embrace WSAN technology. The overarching goal of this project is to accomplish runtime parameter self-adaptation for industrial WSANs in uncertain environment. This project will develop rigorous scientific methods for equipping industrial WSANs with capabilities of optimally configuring themselves based on specific QoS requirements and adapting the configurations at runtime to consistently satisfy the dynamic requirements in uncertain environments. This project aims to advance the state of the art of industrial WSANs through creating a new paradigm of parameter self-adaptation, resulting in improved network performance and better network resource management. This project will also accomplish an increased understanding of the performance tradeoffs that exist in WSANs and enable the development of new solutions to inform users with accurate user-appropriate information on network performance tradeoffs and configuration choices.

工业网络通常连接工业设施中的数百或数千个传感器和执行器，如钢铁厂、炼油厂和化工厂。近年来，人们对无线传感器-执行器网络（WSAN）技术在工业网络中的应用越来越感兴趣。该项目将开发高度自适应的无线传感器网络，实现广泛的工业过程应用，影响经济、安全和生活质量。该项目的成功完成将极大地促进wsan的安装，从而大大提高工业效率，从而显著降低运营成本，从而有助于创造更多的就业机会。该项目的最终目标是将项目成果整合到下一代工业WSAN标准和实际产品中。项目研究结果将在主要的国际会议上提出，并在会议论文集和高影响力期刊上发表，还将用于丰富教育和推广活动。基于IEEE 802.15.4的无线局域网以低功耗运行，制造成本低廉，这使得它们非常适合能源消耗和成本重要的工业过程应用。然而，严格和多样化的服务质量（QoS）需求和动态的工业环境使得管理wsan成为一项艰巨的任务。WSAN管理难题中缺少的一个关键部分是自适应组件，该组件允许WSAN自适应以在不确定环境中一致地满足动态QoS需求。因此，业界对WSAN技术表现出明显的不情愿。本课题的总体目标是实现工业无线局域网在不确定环境下运行时参数的自适应。该项目将开发严格的科学方法，为工业无线局域网提供基于特定QoS要求的最佳配置能力，并在运行时调整配置，以始终满足不确定环境中的动态需求。该项目旨在通过创建参数自适应的新范例来推进工业无线局域网的最新技术，从而提高网络性能和更好的网络资源管理。该项目还将加深对wsan中存在的性能权衡的理解，并使开发新的解决方案能够向用户提供有关网络性能权衡和配置选择的准确用户适当信息。

项目成果

Enabling Cross-technology Communication from LoRa to ZigBee in the 2.4 GHz Band

• DOI: 10.1145/3491222
• 发表时间: 2021-12
• 期刊: ACM Transactions on Sensor Networks (TOSN)
• 作者: Junyang Shi;Xingjian Chen;M. Sha

Enabling Direct Messaging from LoRa to ZigBee in the 2.4 GHz Band for Industrial Wireless Networks

• DOI: 10.1109/icii.2019.00043
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Conference on Industrial Internet (ICII)
• 作者: Junyang Shi;Xingjian Chen;M. Sha

Radio selection and data partitioning for energy-efficient wireless data transfer in real-time IoT applications

• DOI: 10.1016/j.adhoc.2020.102251
• 发表时间: 2020-10
• 期刊: Ad Hoc Networks
• 影响因子: 4.8
• 作者: Di Mu;M. Sha;K. Kang;Hyungdae Yi

Impacts of channel selection on industrial wireless sensor-actuator networks

• DOI: 10.1109/infocom.2017.8057049
• 发表时间: 2017-05
• 期刊: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
• 作者: Dolvara Gunatilaka;M. Sha;Chenyang Lu

Real-Time Information Derivation from Big Sensor Data via Edge Computing

• DOI: 10.3390/bdcc1010005
• 发表时间: 2017-10
• 期刊: Big Data Cogn. Comput.
• 作者: K. Kang;Liehuo Chen;Hyungdae Yi;Bin Wang;M. Sha