Microservices based Framework to Support Interoperable IoT Applications for Enhanced Data Analytics

TL;DR

This paper proposes a microservices-based framework that enables semantic interoperability and advanced data analytics for IoT applications, addressing heterogeneity and scalability challenges.

Contribution

It introduces a novel architecture combining semantic virtualization, microservices, and machine learning for enhanced IoT data management and analytics.

Findings

Successful proof of concept implementation

Improved interoperability among heterogeneous IoT objects

Enhanced data analytics capabilities using knowledge and data-driven techniques

Abstract

Internet of things is growing with a large number of diverse objects which generate billions of data streams by sensing, actuating and communicating. Management of heterogeneous IoT objects with existing approaches and processing of myriads of data from these objects using monolithic services have become major challenges in developing effective IoT applications. The heterogeneity can be resolved by providing interoperability with semantic virtualization of objects. Moreover, monolithic services can be substituted with modular microservices. This article presents an architecture that enables the development of IoT applications using semantically interoperable microservices and virtual objects. The proposed framework supports analytic features with knowledge-driven and data-driven techniques to provision intelligent services on top of interoperable microservices in Web Objects enabled IoT environment. The knowledge-driven aspects are supported with reasoning on semantic ontology models and the data-driven aspects are realized with machine learning pipeline. The development of service functionalities is supported with microservices to enhance modularity and reusability. To evaluate the proposed framework a proof of concept implementation with a use case is discussed.