Autonomous Internet of Things in the Built Environment

建筑环境已经渗透了连接的传感器和执行器，如可编程恒温器，能源表和运动传感器。这些设备的网络被称为物联网（IoT），具有实现一系列应用并提供新的创收机会的巨大潜力。然而，建筑环境中的物联网尚未实现在资源管理、节能运营以及与智能电网和新兴能源系统无缝集成方面取得重大进展的宏伟承诺。这主要是由于缺乏方便的抽象，适当的计算模型和数据驱动的技术。建筑环境中的自主物联网可以通过为建筑物提供先进的监控，实时分析和自动控制功能来实现这一承诺。配备该技术的建筑物将能够（a）监测许多环境方面（状态和事件），（B）与周围环境智能地交互，（c）从通过这些交互获得的经验中学习，（d）在最少的人为监督下做出长期最佳决策，以及（e）以及时有效的方式适应变化。该研究计划旨在从理论和经验两方面了解自主物联网的特性，并研究如何将这种模式应用于建筑物，以改善人类体验，运营效率和更广泛的建筑环境（包括智慧城市）的可持续性。我研究的长期目标是在建筑环境中关闭数据生成，计算和控制之间的循环。为了支持这个长期目标，我的短期研究目标是1。开发分布式操作系统，同时适应实时决策和计算密集型应用的要求; 2.研究如何监测建筑物、居住者和周围环境之间的各种环境因素; 3.研究基于学习的控制策略，该策略依赖于高维传感器数据，以最佳和主动的方式操作建筑物。全球智能建筑和智能城市物联网市场预计将从2017年的63亿美元增长到2026年的222亿美元。我在自主物联网方面的工作将通过开发新的计算模型和分布式系统技术来解决物联网在建筑环境中集成的关键挑战。该研究计划的贡献将有助于改变建筑物与周围环境之间相互作用的性质，并促进新兴能源系统在建筑物中的采用。该研究计划非常符合加拿大科技行业的战略重点，并为分布式系统，传感器网络和机器学习领域的研究生提供实践培训。

The built environment has been permeated by connected sensors and actuators, such as programmable thermostats, energy meters, and motion sensors. The network of these devices, dubbed as the Internet of Things (IoT), has significant potential to enable a range of applications and present new revenue generation opportunities. However, IoT in the built environment has not yet fulfilled the grand promise of significant advances in resource management, energy-efficient operations, and seamless integration with the smart grid and emerging energy systems. This is mainly due to the lack of convenient abstractions, appropriate computational models, and data-driven techniques. Autonomous IoT in the built environment can fulfill this promise by providing advanced monitoring, real-time analytics, and automatic control capabilities to buildings. Buildings equipped with this technology will be capable of (a) monitoring numerous environmental facets (states and events), (b) interacting intelligently with the surrounding environment, (c) learning from experiences gained through these interactions, (d) making decisions that are optimal in the long run with minimum human supervision, and (e) adapting to changes in a timely and efficient manner. This research program aims to understand, both theoretically and empirically, the properties of autonomous IoT and investigate how the application of this paradigm to buildings can improve human experience, operational efficiency, and sustainability of the broader built environment including smart cities. The long-term goal of my research is to close the loop between data generation, computation, and control in the built environment. In support of this long-term goal, my short-term research objectives are to 1. develop a distributed operating system that simultaneously accommodates the requirements of real-time decision making and compute-intensive applications; 2. study how diverse environmental facets encapsulating interactions between the building, occupants, and the surrounding environment can be monitored; 3. investigate learning-based control strategies which rely on high-dimensional sensor data to operate the building in an optimal and proactive fashion. The global market of IoT for smart buildings and smart cities is expected to grow from $6.3 billion in 2017 to $22.2 billion in 2026. My work on autonomous IoT would address the key challenges that concern the integration of IoT in the built environment by developing new computational models and distributed systems technology. The contributions of this research program would help to transform the nature of interactions between buildings and the surrounding environment, and would facilitate the adoption of the emerging energy systems in buildings. This research program fits well within the strategic priorities of Canada's tech industry and provides practical training for graduate students in the areas of distributed systems, sensor networking, and machine learning.