Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures

TL;DR

This paper presents a dependable, scalable IoT data processing platform for urban infrastructure monitoring, integrating open-source technologies and demonstrating its effectiveness through a prototype on Kubernetes.

Contribution

It introduces a comprehensive architecture for dependable IoT data processing tailored for urban infrastructure, with general methods for data enrichment and analysis.

Findings

Effective data processing demonstrated on Kubernetes

Dependable platform meets urban monitoring requirements

Open-source components enable scalable architecture

Abstract

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.