CCSS: Learning-Driven Scheduling and Communications in Edge-Assisted Battery-Free Wireless Sensor Networks

CCSS：边缘辅助无电池无线传感器网络中的学习驱动的调度和通信

• 批准号: 2011845
• 负责人: Wei Li
• 金额: $ 38.09万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011845&HistoricalAwards=false
• 关键词: CCSS; Learning; Driven; Scheduling; Communications

The recent significant progresses of information-sensing techniques, wireless communications, and embedded systems have greatly accelerated the generation of sensory data, which provides a nourishing fertile ground for the development of deep learning and machine learning in data-hungry applications. Nevertheless, the inherent limitations of traditional Wireless Sensor Networks (WSNs) including limited lifetime, difficult battery replacement, and centralized network architecture, become an unavoidable obstacle to the wide deployment and adoption of sensory data. Moreover, due to the big volume and high complexity of sensory data, processing sensory data in a centralized manner would increase the consumption of network resources and the risk of privacy leakage. To tackle the aforementioned limitations as well as to satisfy the needs of managing massive sensory data in real applications, developing power-optimized, sustainably-reliable, and efficiently-distributed solutions has become an essential task.This project explores the energy characteristics of battery-free WSNs, capacities of edge-assisted sinks, and advantages of distributed multi-task learning, which will result in the following technical innovations. (1) The seamless integration of battery-free sensors, edge/cloud computing and machine learning can break through technique imprisonments in traditional WSNs, in which new problems are defined and new methodologies are developed. (2) The energy correlation of battery-free sensors in the temporal and spatial domains is exploited to predict sensor’s dynamic sensing ability for scheduling sensing activities, in which the schemes of time-energy-correlated sensing, time-space cooperative data acquisition and energy-accompanied data acquisition will be developed. (3) The interference of battery-free sensors in the temporal, spatial and energy domains is utilized to construct multi-dimensional conflict graphs for interference-free transmission scheduling, in which the algorithms of associating battery-free sensors with edge-assisted sinks and scheduling data collection from sensors to sinks will be designed. (4) The diverse capacities of computation and communication on edge-assisted sinks are employed to schedule learning process so that multiple related tasks can be simultaneously completed in a distributed fashion. (5) The validation is well planned, where an analog simulator and a prototype system will be built to perform the designed simulations and real-data experiments, respectively.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.

信息感应技术，无线通信和嵌入式系统的最新重大进展极大地加速了感官数据的产生，这为在数据繁殖应用中开发深度学习和机器学习提供了滋养的基础。然而，传统无线传感器网络（WSN）的继承局限性在内，包括寿命有限，电池更换和集中式网络体系结构，成为了广泛部署和采用感觉数据的不可避免的障碍。此外，由于感觉数据的量很大和高复杂性，以集中式的方式处理感官数据将增加网络资源的消耗和隐私泄漏的风险。 To tackle the approximate limitations as well as to satisfy the needs of managing massive sensory data in real applications, developing power-optimized, sustainably-reliable, and efficiently-distributed solutions has become an essential task.This project explores the energy characteristics of battery-free WSNs, capacity of edge-assisted sinks, and advantages of distributed multi-task learning, which will result in the following technical innovations. （1）无电池传感器，边缘/云计算和机器学习的无缝集成可以破坏传统WSN的技术监禁，其中定义了新问题并开发了新方法。 （2）利用临时和空间结构域中无电池传感器的能量相关性，以预测传感器的动态灵敏度，以安排灵敏度活动，在该活动中，将开发与时间能量相关的传感器，时空合作数据采集和能量ACPANIED数据采集的方案。 （3）在临时，空间和能量域中无电池传感器的干扰用于构建用于无干扰传输计划的多维冲突图，其中将无电池传感器与边缘辅助的水槽和调度数据收集的数据收集的算法相关联。 （4）使用边缘辅助水槽上的计算和通信能力来安排学习过程，以便以分布式方式轻松完成多个相关任务。 （5）验证是精心计划的，将分别建立模拟模拟器和原型系统来执行设计的模拟和真实数据实验。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地认为通过评估。

项目成果

Towards Neural Network-Based Communication System: Attack and Defense

• DOI: 10.1109/tdsc.2022.3203965
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Dependable and Secure Computing
• 影响因子: 7.3
• 作者: Zuobin Xiong;Zhipeng Cai;Chun-qiang Hu;Daniel Takabi;Wei Li

Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT

• DOI: 10.1109/tii.2021.3073925
• 发表时间: 2022-02-01
• 期刊: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
• 影响因子: 12.3
• 作者: Xiong, Zuobin;Cai, Zhipeng;Li, Wei
• 通讯作者: Li, Wei

Multi-Source Adversarial Sample Attack on Autonomous Vehicles

• DOI: 10.1109/tvt.2021.3061065
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Zuobin Xiong;Honghui Xu;Wei Li;Zhipeng Cai

Privacy-Preserving Mechanisms for Multi-Label Image Recognition

• DOI: 10.1145/3491231
• 发表时间: 2022
• 期刊: ACM Trans. Knowl. Discov. Data
• 作者: Honghui Xu;Zhipeng Cai;Wei Li

Federated Generative Model on Multi-Source Heterogeneous Data in IoT

• DOI: 10.1609/aaai.v37i9.26252
• 发表时间: 2023-06
• 作者: Zuobin Xiong;Wei Li;Zhipeng Cai