CAREER: When Energy Harvesting Meets "Big Data": Designing Smart Energy Harvesting Wireless Sensor Networks

由于能量收集技术的最新进展，为传感器节点提供能量收集设备成为一种自然而可行的解决方案，以构建一个可自我维持的无线传感器网络。然而，无线传感器网络通常需要收集和传输海量数据。智能地利用随机、不均匀、稀缺的收获能源，来满足“大数据”带来的能源需求，是极具挑战性的。数据传输的实时延迟要求使问题变得更加复杂。该项目通过战略性地分配随机能量资源来收集和传输最重要的传感器数据，从而克服了这些挑战，为能量采集传感器网络中的数据收集、传输和分析提供了一条可靠的管道。预计该项目将对收集能量的无线传感器网络的设计和广泛部署产生直接影响，应用于无线电频谱管理、环境监测、医疗保健、监控、救灾等。此外，拟议的工作具有远远超出收集能量的无线传感器网络的广泛潜在应用，如智能电网中的自动居民能源消耗调度、考虑可再生能源投入的微电网规划、从大规模参与式传感收集的天文量数据中提取信息、建议的项目整合了一个具有很强教育成分的研究议程，并将服务于以下教育目的：1)通过本科生研究、高级项目设计和基于项目的学习，PI将利用建议的项目来激发和保持本科生对工程学的兴趣；2)申请的拨款将用于通过课程开发、适当的研究指导和行业合作来培训研究生；以及3)拟议的项目将用于通过各种外展活动吸引潜在的工程学学生。本项目的目标是在数据密集型能量收集无线传感器网络中构建一种新的感知和传输方案，以智能地利用随机、非均匀和稀缺的收集能量，并提供可分析证明的感知、传输和推理性能保证。为了实现这一目标，提出了两种不同但紧密耦合的方法。一种是能源驱动的方法，另一种是数据驱动的方法。对于能量驱动的方法，利用能量收集过程的统计信息来构建在线感知和传输方案。本文研究的两个主要任务是针对大规模传感器网络能量供应不均匀的面向目标的协作感知调度策略和满足实时时延要求的时延受限数据传输方案。数据驱动的方法利用基本传感现象的特点，自适应地和战略性地分配稀缺的能源资源，用于收集和传输最重要的传感器数据。这项研究的两个具体任务包括基于高斯过程的框架，以系统地利用传感领域中的时空相关性，以及自适应传感策略，其利用底层传感信号的结构稀疏性，两者都在传感器的随机能量约束下。该项目承诺在坚实的分析基础上建立具有卓越感知、传输和推理性能的智能能量采集无线传感器网络。基于时延约束的集能通信信息论分析将把更新理论中的一套新的分析工具与信息论中的分析工具结合起来，并产生协同效应。所提出的数据驱动的自适应传感方法将发展随机排队控制和高维数据分析之间的协同效应。这项研究的跨学科性质使我们能够利用随机排队控制、更新理论、信息论、机器学习等技术，并有望促进对这些领域的理解。

In order to build a self-sustainable wireless sensor network, powering sensor nodes with energy harvesting devices becomes a natural and feasible solution, thanks to the recent progress on energy harvesting technology. However, wireless sensor networks usually need to collect and transmit vast amounts of data. Utilizing the random, non-uniform, and scarce harvested energy intelligently to meet the energy demand caused by "big data" is extremely challenging. The real-time latency requirement for data delivery makes the problem even more complicated. This project overcomes these challenges through strategically allocating the stochastic energy resources to collect and transmit the most important sensor data, providing a reliable pipeline for data collection, transmission and analysis in energy harvesting sensor networks. The project is expected to have direct impact on the design and wide deployment of energy harvesting wireless sensor networks, with applications in radio spectrum management, environment monitoring, healthcare, surveillance, disaster relief, etc. Furthermore, the proposed work has widespread potential applications far beyond energy harvesting wireless sensor networks, such as automatic residential energy consumption scheduling in smart grid, micro-grid planning with renewable energy inputs, information extraction from astronomical amounts of data collected from large-scale participatory sensing, etc. The proposed project integrates a research agenda with a strong educational component and will serve the following educational purposes: 1) the PI will use the proposed project to stimulate and maintain undergraduate students' interests in engineering via undergraduate research, senior project design and project based learning; 2) the requested funding will be used to train graduate students via curriculum development, proper research mentoring and industry collaboration; and 3) the proposed project will be used to attract potential engineering students through various outreach activities. The goal of this project is to construct a new paradigm of sensing and transmission schemes in data-intensive energy harvesting wireless sensor networks to intelligently utilize the random, non-uniform, and scarce harvested energy with analytically provable sensing, transmission and inference performance guarantees. Two different but closely coupled approaches are proposed to achieve this goal. One is an energy-driven approach and the other is a data-driven approach. For the energy-driven approach, the statistics of the energy harvesting process are exploited to construct online sensing and transmission schemes. Two main tasks addressed in this research thrust are objective-oriented cooperative sensing scheduling policies to cope with the non-uniform energy supply in large-scale sensor networks, and delay-constrained data transmission schemes to meet the real-time latency requirement. The data-driven approach utilizes the characteristics of the underlying sensing phenomena to adaptively and strategically allocate scarce energy resources for the collection and transmission of the most important sensor data. Two specific tasks in this research thrust include a Gaussian process based framework to systematically utilize the spatial-temporal correlations in sensing fields, and adaptive sensing strategies that exploit the structured sparsity of underlying sensing signals, both under the stochastic energy constraints at sensors. The project promises to build intelligent energy harvesting wireless sensor networks with superb sensing, transmission and inference performances on a solid analytical foundation. The delay-constrained information theoretic analysis for energy harvesting communications will integrate a new set of analytical tools from renewal theory with tools in information theory, and create synergies between them. The proposed data-driven adaptive sensing approach will develop synergies between stochastic queueing control and high-dimensional data analysis. The interdisciplinary nature of the research allows us to utilize techniques from stochastic queueing control, renewal theory, information theory, machine learning and is expected to advance the understanding of those areas.