The Internet of Things describes a network of physical devices interacting and producing vast streams of sensor data. At present there are a number of general challenges which exist while developing solutions for use cases involving the monitoring and control of urban infrastructures. These include the need for a dependable method for extracting value from these high volume streams of time sensitive data which is adaptive to changing workloads. Low-latency access to the current state for live monitoring is a necessity as well as the ability to perform queries on historical data. At the same time, many design choices need to be made and the number of possible technology options available further adds to the complexity. In this paper we present a dependable IoT data processing platform for the monitoring and control of urban infrastructures. We define requirements in terms of dependability and then select a number of mature open-source technologies to match these requirements. We examine the disparate parts necessary for delivering a holistic overall architecture and describe the dataflows between each of these components. We likewise present generalizable methods for the enrichment and analysis of sensor data applicable across various application areas. We demonstrate the usefulness of this approach by providing an exemplary prototype platform executing on top of Kubernetes and evaluate the effectiveness of jobs processing sensor data in this environment.
翻译:“物联网”描述一个物理装置互动和产生大量感官数据流的网络,目前存在一些一般性挑战,在为监测和控制城市基础设施的个案制定可靠的IoT数据处理平台的同时,还存在一些一般性挑战,其中包括需要一种可靠的方法,从这些大量时间敏感数据流中提取价值,以适应不断变化的工作量。对当前状态进行现场监测的低纬度访问是必要的,也是对历史数据进行查询的能力。与此同时,需要做出许多设计选择,而且可能提供的技术选项的数量进一步增加了复杂性。我们在本文件中为监测和控制城市基础设施提供了一个可靠的IoT数据处理平台。我们从可靠性的角度界定了要求,然后选择了一些成熟的开放源技术来满足这些要求。我们审查了提供整体整体结构所需的不同部分,并描述了每个组成部分之间的数据流。我们同样提出了适用于不同应用领域的传感器数据的浓缩和分析的通用方法。我们通过在Kubernetes顶端提供一个堪称样板的原型平台来展示这一方法的效用,并评估这一环境中的传感器处理工作效率。