CAREER: Survivable, Maintainable, and Adaptable Sensor Networks

该学院早期职业发展计划(CALEAR)奖将通过研究复杂无线传感器网络的高效运行，为促进国家繁荣和增强国家安全做出贡献。无线传感器网络(WSN)越来越多地被用于收集和处理各种领域的空间和时间上的数据。无线传感器网络出现在生态系统和地质现象中，以及监测生物、化学或核武器的敌对使用。在所有这些应用中，WSN必须在较长时间内提供有效覆盖，即使单个传感器出现故障并需要维护也是如此。获取和/或安装传感器的成本可能很高，这引发了如何确保无线传感器网络无限期地获得令人满意的性能的问题。该奖项支持研究，以建立随着时间的推移无线传感器网络的设计和维护所需的方法，因为它正在发展以满足不断变化的条件。该奖项还将支持在PI的母校内创建一个新的跨学科荣誉课程。这个项目将帮助工程专业的学生通过扩大他们对现代社会问题新出现的解决方案的认识来解决多学科问题。这项研究将随着时间的推移对设计、维护和适应临时无线传感器网络的复杂问题做出贡献。这个问题需要大量的计算复杂性来源，这些复杂性源于对随机演变的网络和随着时间推移所做的决策进行建模。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改进数学和计算方法。该研究将首先开发随机网络维护模型和易于计算的可靠性估计程序，以描述在给定维护策略下的无线传感器网络可靠性。优化方法将纳入新出现的可靠性表示/估计技术，以确定在成本和可靠性标准方面高效的固定时间表和基于状态的有效维护策略。该奖项反映了NSF的法定使命，并已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。

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.