Edge-Cloud Computing for Internet of Things Data Analytics

互联设备的数量正以前所未有的速度增长，导致在设备、存储和处理位置之间移动的物联网(IoT)数据呈爆炸式增长。来自传感器的数据通常被传输到云中进行存储和分析，然后交付给各种软件应用程序。例如，来自智能家居的数据被传输到可能远在数千英里之外的数据中心，然后再返回到家中的设备上显示。由于数据中心流量已经以ZB计算，尝试将所有物联网数据传输到云将使通信网络紧张并增加成本。此外，在安全关键型应用中常见的实时系统无法容忍传输到云的延迟。 为了应对这些物联网挑战，拟议的研究计划将结合边缘和云计算。云计算将数据传输到计算位置，而边缘计算使计算更接近生成数据的网络边缘。这里，术语边缘包括测量设备(例如，传感器、智能手机)和雾节点，即靠近数据源(例如，网关、本地服务器)的资源。边缘云方法将减少网络流量和延迟，改善用户体验，实现离线运营，并减少与云相关的安全风险。 该研究计划的长期目标是通过将物联网数据分析扩展到边缘计算来推进该领域。短期目标包括： 1.为智能城市启用边缘云物联网数据分析将通过为能源管理和可持续建筑等特定智能城市使用案例采用边缘计算来启动边缘云分析工作。 2.设计资源管理模型以跨边缘云环境的不同节点分配和管理计算，以最小化响应时间和/或网络流量。 3.使物联网互操作性能够整合来自不同物联网设备的数据和非物联网数据，通过边缘计算支持新的数据分析方法和新的软件应用。 4.设计物联网边缘-云数据管理方法，将数据存储分布在边缘和云节点上，通过在靠近生产(边缘)或消费(边缘或云)的位置存储或复制数据来减少网络流量和响应时间。 拟议的研究将支持新的物联网应用和服务，这些应用和服务目前由于数据传输到云的延迟或成本而无法实现。软件应用程序将从改进的服务质量、将计算负载转移到附近设备的能力以及增强的位置感知中受益。使用物联网分析的公司将通过流程优化、洞察发现和改进决策而受益。这一研究计划将有助于加拿大在这一快速增长的领域发挥领导作用。

The number of connected devices is growing at an unprecedented rate, creating an explosion of Internet of Things (IoT) data that are moving among devices, storage, and processing locations. Data from sensors are typically transferred to the cloud for storage and analysis and then delivered to various software applications. For example, data from a smart home are transferred to a data centre possibly thousands of miles away and then back again for display on devices within the home. With data centre traffic already measured in zettabytes, attempting to transfer all IoT data to the cloud will strain communication networks and increase cost. Moreover, real-time systems, which are common in safety critical applications, cannot tolerate latencies caused by transfer to the cloud. To address these IoT challenges, the proposed research program will combine edge and cloud computing. Whereas cloud computing transfers data to the computing location, edge computing brings computation closer to the network edge where data are generated. Here, the term edge encompasses measuring devices (e.g., sensors, smart phones) and fog nodes, the resources close to data sources (e.g., gateways, local servers). Edge-cloud approaches will decrease network traffic and latencies, improve user experience, enable off-line operation, and reduce exposure to cloud-related security risks. The long term objective of this research program is to advance the field of IoT data analytics by extending it to edge computing. The short-term objectives include: 1. Enabling Edge-Cloud IoT Data Analytics for Smart Cities will initiate work on edge-cloud analytics by adopting edge computing for specific smart city use cases such as energy management and sustainable buildings. 2. Designing Resource Management Models to allocate and manage computation across diverse nodes of the edge-cloud environment to minimize response time and/or network traffic. 3. Enabling IoT Interoperability to integrate data from diverse IoT devices and non-IoT data in support of novel data analytics methods and new software applications through edge computing. 4. Designing IoT Edge-Cloud Data Management Methods that will distribute data storage over edge and cloud nodes to reduce network traffic and response time by storing or replicating data close to where they are produced (edge) or consumed (edge or cloud). The proposed research will enable new IoT applications and services that are currently not possible due to latencies or costs associated with data transfer to the cloud. Software applications will benefit through improved quality of service, ability to offload computation to nearby devices, and increased location awareness. Companies using IoT analytics will benefit through process optimization, insights discovery, and improved decision-making. This research program will contribute to Canadian leadership in this rapidly growing field.