Green software-defined platform for smart city big data applications

到2050年，世界上70%以上的人口将居住在只占世界土地面积2%、消耗世界资源75%的城市，这将给如何保持经济发展、环境可持续性和社会弹性带来挑战。智慧城市技术（云计算、软件定义网络、物联网）可以帮助解决这些挑战，通过启用开放的软件应用平台，使城市能够应对交通拥堵、空气和噪音污染、安全和犯罪、应急响应、气候变化、经济增长和城市服务交付等挑战。我们的目标是设计和部署一个智慧城市应用平台，展示智慧城市应用。在一个典型的应用中，传感器收集有关人类活动、城市系统和环境的数据，云计算制定协调有益行为的计划，网络将控制信息传递给执行器。例如，智能交通应用程序可以协调一个地区的所有出行方式，从而优化出行时间，最大限度地减少能耗、污染和碳足迹。其他应用将涉及自动驾驶汽车、能源生产和消耗，以最大限度地减少碳足迹、应急响应以及透明的社区仪表板，以反映城市的成果和绩效。在这项研究中，我们将整合地理分布的传感、网络、计算和驱动资源的管理，以创建一个平台，支持私人和公共服务提供商以及公民个人的智慧城市应用。这需要发展：1。一个安全可靠的软件定义信息结构，可以收集传感器数据（同时确保隐私），提供计算以提取意义和设计策略，并及时执行控制动作；2. 用于在此结构上部署应用程序的框架；3. 为智能应用程序提供分析、机器学习和算法支持的商业智能服务。我们将在沃恩、纽马克特、多伦多和蒙特利尔展示在交通、空气质量、建筑和人群管理方面的智能应用。

By 2050, over 70% of the world's population will live in cities that occupy only 2% of the world's land mass and consume 75% of its resources, leading to challenges on how to maintain economic advancement, environmental sustainability, and social resiliency. Smart city technologies (cloud computing, software-defined networking, Internet of Things) can help address these challenges by enabling open software application platforms that allow cities to tackle challenges such as traffic congestion, air and noise pollution, safety and crime, emergency response, climate change, economic growth, and delivery of city services. Our goal is to design and deploy a smart city application platform and to demonstrate smart city applications. In a typical application, sensors gather the data on human activity, urban systems and the environment, cloud computing formulates a plan for coordinated beneficial behaviour, and networking carries control messages to actuators. For example, a smart transportation application may coordinate all modes of travel in a region so that travel times are optimized and energy consumption, pollution and carbon footprint are minimized. Other applications will address autonomous vehicles, energy generation and consumption to minimize carbon footprint, emergency response, and transparent community dashboards for city outcomes and performance. In this research we will integrate the management of geographically-distributed sensing, networking, computing, and actuation resources to create a platform that supports smart city applications for private and public service providers as well as individual citizens. This entails developing: 1. A secure and reliable software-defined information fabric that can collect sensor data (while ensuring privacy), provide computing to extract meaning and devise strategies, and execute control actions in timely fashion; 2. A framework for deploying applications on this fabric; 3. Business-intelligence services that offer analytics, machine learning, and algorithmic support for smart applications. We will demonstrate smart applications in transportation, air quality, buildings, and crowd management in Vaughan, Newmarket, Toronto, and Montreal.