Scalable IoT Solution using Cloud Services – An Automobile Industry Use Case

Abstract

The role of IoT and related internet-based applications in otherwise mechanical devices to monitor, manage and enhance the performance of the same is quite widespread now. Almost all public cloud service providers provide scalable, fully managed and elastic IoT related services. The data flows from these services are essentially streaming and can be consumed for further use in various predictive, descriptive and visualization modules. The cloud platforms enable ingestion, transformation and usage of the data by providing streaming, machine learning and sharable visualization services. This ecosystem greatly reduces the time to create IoT based minimum viable product creation which in turn enhances the business value realization cycle. The effect of cycle time reduction to design, architect and develop IoT solutions leads to a rapid improvement of business lead time and makes it easier for businesses to gain from the data insights and plan the next course of action. In this paper, one such enterprise graded use case is explored, in which the Azure IoT platform in terms of the offerings and associated ecosystem of Azure Stream Analytics and Azure Machine learning services are explained. This paper covers design, architecture, development and deployment of the solution prepared and how the same is monitored once in production. Security is a very important aspect of the same and here the security architecture is being explored. A conclusion is presented with the scope of future enhancements using auto ML services in serverless platforms to enable real-time automated decision making augmented with human expertise and intelligence.