A Model-Driven Framework for Heterogeneous IoT Systems

Abstract

The development of data-driven smart cities has been primarily supported by the Internet of Things (IoT) paradigm, with sensors and actuators playing a critical role in a myriad of applications. However, as IoT Fragmentation becomes a reality due to diverse hardware and networking standard settings, interoperability among heterogeneous IoT devices remains a persistent challenge. This dissertation proposes a holistic approach for achieving hardware-level interoperability at the edge layer, leveraging the World Wide Web Consortium’s (W3C) Web of Things (WoT) standard as a semantic and architectural foundation.

A novel system framework is introduced, featuring a suite of modular software components for both edge servers and constrained end nodes. These components collectively enable dynamic overthe-air updates, automated onboarding of new devices, and remote self-identification capabilities. By embedding WoT-based data models within devices and edge services, the framework supports semantic integration across heterogeneous hardware and communication protocols, facilitating coherent and flexible interaction across system components.

A formal application programming interface (API) and a relational Thing Model schema are also proposed to structure and manage device behavior and affordances. The solution is validated through a proof-of-concept implementation that demonstrates the feasibility of the proposed framework through a practical implementation that fulfills the core technical requirements established by the system’s conceptual design. While designed with smart cities as the primary application domain, the architectural principles and software components developed in this work are broadly applicable to areas such as Industry 4.0, smart agriculture, and other cyber-physical systems.

Preliminary results from this work have been peer-reviewed and accepted for presentation at the 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2025), confirming its relevance to the broader IoT and cyber-physical systems research communities.