Generative Signal Processing and Data Fusion for Sensor Networks

物联网（IoT）和传感器之间的分析和互联性的最新趋势正在改变个性化医疗保健、农业、制造业、能源和监控等行业，使其能够自主处理数据，以最小的延迟收集见解并做出明智的决策。市场研究表明，到2020年，连接传感器的数量预计将达到208亿个，预计未来5年传感器市场的投资将达到6万亿美元。虽然传感器技术的发展旨在提高分辨率和效率，但成本和物理障碍可能限制传感器的实际部署。由于传感的限制，由于各种因素，包括传感器故障、采样或分辨率、干扰、物体遮挡或天气或夜间的能见度问题，经常无法完全观察到现象。这些在空间、时间和传感器模式上的信息缺失是实时连续传感的障碍。这激发了开发传感图像的信号处理工具的需求，而不受物理传感约束的限制。我们提出了一个生成信号处理和数据融合框架，以推动传感的边界。我们的目标是为生成传感和处理的三个基本方面开发一套统一的技术。我们提出了理论方法和模型来寻找丢失信号，对象和轨道的解决方案。我们将研究用生成信号处理增强检测、跟踪和识别处理任务的算法，并将这些过程结合起来。最后，我们提出了一个人工传感器概念和数据融合方法，将弥合人工和真实物理传感之间的差距。研究结果在一个框架，以产生，在所需的时间，空间位置和选定的传感器模式处理数据。这将在传感方面对公众有利，因为将发展个性化和方法，以便在目前可能昂贵或有限的情况下获取所需的信息。我们与学术和工业合作伙伴验证遥感，城市和管道监测方面的应用，以推动未来的商业技术，为传统方法无法感知的现象开发下一代传感。对加拿大的好处包括使连续传感的最终趋势更具成本效益的一般方法，衍生的算法用于管道，公共安全的声学监测，海洋或北极监测等应用。该研究将增强感知故障场景下决策的鲁棒性，以补充当前的物理传感器并实现连续感知。

The recent trends in analytics and interconnectivity between internet of things (IoT) and sensors is transforming industries such as personalized healthcare, agriculture, manufacturing, energy, and surveillance to process data autonomously to gather insight and make informed decisions with minimal delay. Market studies suggest the number of connected sensors is expected to reach 20.8 billion by 2020, and $6 trillion investment is predicted for the sensing market over the next 5 years. Although there exists development in sensor technologies aimed at increasing resolution and efficiency, cost and physical barriers may limit the practical sensor deployment. Due to sensing limitations, phenomena frequently cannot be observed completely due to various factors including sensor failures, sampling or resolution, interference, occlusions from objects, or visibility issues from weather or night-time. These missing information in space, time, and sensor modalities are barriers to real-time continuous sensing. This motivates the need for developing signal processing tools for the sensing picture without being limited by physical sensing constraints. We propose a generative signal processing and data fusion framework to push the boundaries of sensing. Our goal is to develop a unified set of technologies for three fundamental aspects of generative sensing and processing. We propose theoretical approaches and models to find solutions to missing signals, objects, and tracks. We will investigate algorithms for augmenting detection, tracking, and recognition processing tasks with generative signal processing and combine these processes. Finally, we propose an artificial sensor concept and data fusion approaches that will bridge the gap between artificial and real physical sensing. The research results in a framework to generate, process data at desired times, spatial locations, and selected sensor modalities. This will be publicly beneficial in sensing, as personalization and approaches will be developed to acquire desired information in cases where it may be currently expensive or limited. We verify applications in remote sensing, urban and pipeline monitoring with academic and industrial collaborators to drive future commercial technologies, develop next generation sensing for phenomena which cannot be sensed by conventional approaches. Benefits to Canada include a general approach to make eventual trends in continuous sensing more cost-effective, derived algorithms for applications like pipeline, acoustic monitoring for public safety, maritime or arctic monitoring. The research will enhance robustness of decision making in scenarios of sensing failure, to complement current physical sensors and enable continuous sensing.