Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems.

Area of Science:

• Computer Science
• Engineering
• Information Technology

Background:

• The Internet of Things (IoT) is rapidly expanding, with numerous platforms offering diverse business models.
• Platform heterogeneity hinders the creation of large-scale IoT ecosystems and cross-platform applications.
• Existing solutions lack effective tools for integrating services from disparate IoT platforms.

Purpose of the Study:

• To introduce the Data Spine, a federated platform enabler designed to address IoT interoperability gaps.
• To facilitate the creation of an extensible ecosystem of heterogeneous IoT platforms, specifically within the manufacturing domain.
• To demonstrate the performance and advantages of the Data Spine over traditional multi-platform integration methods.

Main Methods:

• Development of a reference implementation for the Data Spine federated platform enabler.
• Quantitative evaluation of the Data Spine's performance and capabilities.
• Comparative analysis against traditional approaches for integrating multiple IoT platforms.

Main Results:

• The Data Spine successfully bridges interoperability gaps in heterogeneous IoT environments.
• The platform enables extensible integration of new tools, services, and platforms.
• Evaluation demonstrates adequate system performance and significant advantages over traditional methods.

Conclusions:

• The Data Spine is a viable solution for creating unified IoT ecosystems in manufacturing.
• It offers benefits such as single sign-on and a low-code environment for simplified cross-platform application development.
• The Data Spine promotes innovation and efficiency by enabling easier composition of IoT tools and services.