CAREER: Survivable, Maintainable, and Adaptable Sensor Networks

职业：可生存、可维护和适应性强的传感器网络

• 批准号: 1751191
• 负责人: Kelly Sullivan
• 金额: $ 50万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751191&HistoricalAwards=false
• 关键词: CAREER; Survivable; Maintainable; Adaptable; Sensor

This Faculty Early Career Development Program (CAREER) award will contribute to the advancement of national prosperity and increased national security by studying efficient operations of complex wireless sensor networks. Wireless sensor networks (WSNs) are increasingly used to collect and process data over space and time in a variety of domains. Wireless sensor networks appear in ecosystems and geological phenomena, as well in monitoring adversarial use of biological, chemical, or nuclear weapons. In all of these applications, the WSN must provide effective coverage over an extended period of time even as individual sensors fail and require maintenance. Acquiring and/or installing sensors may be costly, raising the question of how to ensure satisfactory performance of the WSN indefinitely. This award supports research to establish methodology required for the design and maintenance of the WSN over time as it evolves to meet changing conditions. The award will additionally support the creation of a new interdisciplinary honors curriculum within the PI's home institution. This program will prepare engineering students to tackle multi-disciplinary problems by broadening their awareness of emerging solutions to modern societal problems.This research will contribute to the complex problem of designing, maintaining, and adapting ad hoc WSNs over time. This problem entails numerous sources of computational complexity stemming from modeling randomly evolving networks and decisions made over time. Overcoming these challenges will require advancing mathematical and computational methods from network optimization, complex system reliability, Monte Carlo simulation, stochastic processes, and stochastic optimization. The research will initially develop stochastic network maintenance models and computationally tractable reliability estimation procedures for the purpose of describing WSN reliability under a given maintenance policy. Optimization methodology will be derived to incorporate emerging reliability representation/estimation techniques in order to determine efficient fixed-schedule and condition-based maintenance policies that are efficient with respect to cost and reliability criteria.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.

该学院早期职业发展计划（Career）奖将通过研究复杂无线传感器网络的有效运作，为促进国家繁荣和提高国家安全做出贡献。无线传感器网络（WSNs）越来越多地用于收集和处理各种领域的空间和时间数据。无线传感器网络出现在生态系统和地质现象中，也出现在监测生物、化学或核武器的对抗性使用中。在所有这些应用中，即使单个传感器发生故障并需要维护，WSN也必须在较长时间内提供有效的覆盖。获取和/或安装传感器可能是昂贵的，这就提出了如何无限期地确保WSN令人满意的性能的问题。随着时间的推移，随着WSN的发展，以满足不断变化的条件，该奖项支持研究建立设计和维护所需的方法。该奖项还将支持在PI所在机构内创建新的跨学科荣誉课程。该课程将通过拓宽他们对现代社会问题新兴解决方案的认识，为工程专业学生解决多学科问题做好准备。随着时间的推移，本研究将有助于解决设计、维护和适应自适应无线传感器网络的复杂问题。这个问题涉及许多计算复杂性的来源，这些复杂性源于对随机进化的网络和随时间做出的决策的建模。克服这些挑战将需要从网络优化、复杂系统可靠性、蒙特卡罗模拟、随机过程和随机优化等方面推进数学和计算方法。该研究将首先开发随机网络维护模型和可计算的可靠性估计程序，以描述给定维护策略下WSN的可靠性。将衍生出优化方法，以结合新兴的可靠性表示/估计技术，以确定有效的固定时间表和基于状态的维护策略，这些策略在成本和可靠性标准方面是有效的。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。

项目成果

Condition-Based Node Deployment Policies for Reliable Wireless Sensor Networks

可靠无线传感器网络的基于条件的节点部署策略

• 发表时间: 2021
• 期刊: Proceedings of the 2021 IISE Annual Conference
• 作者: Boardman, Nicholas T.;Sullivan, Kelly M.
• 通讯作者: Sullivan, Kelly M.

Approximate Dynamic Programming for Selective Maintenance in Series–Parallel Systems

• DOI: 10.1109/tr.2019.2916898
• 发表时间: 2020-09
• 期刊: IEEE Transactions on Reliability
• 影响因子: 5.9
• 作者: Khatereh Ahadi;K. M. Sullivan

Time-Based Node Deployment Policies for Reliable Wireless Sensor Networks

可靠无线传感器网络的基于时间的节点部署策略

• DOI: 10.1109/tr.2020.3047757
• 发表时间: 2021
• 期刊: IEEE Transactions on Reliability
• 影响因子: 5.9
• 作者: Boardman, Nicholas T.;Sullivan, Kelly M.
• 通讯作者: Sullivan, Kelly M.