Est 2011
T: + 353 (0) 87 778 2695

IoTLEGO – Interoperability Made Easy and Agile

IoTLEGO – Interoperability Made Easy and Agile

Our IOTLEGO solution is about enabling an “easy” and “agile” Data Harvesting pipeline to feed into our fast and scalable Data Analysis engine.



IoTLEGO is the result of 8 years of research into applying Semantic Web technologies to solve the interoperability issue caused by 400+ protocols and thousands of IoT Platforms, IoT hardware vendors. IoTLEGO overcomes the problem of vendor and platform lock-ins by enabling transparent integration across systems and devices.



IoTLEGO solution is a unified IoT multi-protocol software stack. Figure 1 shows its unique characteristic parts. This software stack is based on four layers commencing with Data Virtualization of IoT data sources provided by various platforms and hardware, called the Graph of Things (GoT). GoT is based on Semantic Web technology which makes sense of intricate relationships and interdependencies across systems via the Graph Processing Engine (GPE).

GPE enables faster real-time analytics in comparison to classical database techniques e.g. complex queries of up 40 database joins can be created. IoTLEGO is scalable and addresses that fact that there is no universal solution to tie the billions of sensor data into an intelligent system with a platform for every
application domain, scale and infrastructure.

IoTLEGO provides a Visual IoT Mashup Builder and Analytics Tools. It has an easy to use drag and drop editor. This agility is facilitated by the nature of the graph structure which can be easily expanded to cope with the introduction of new devices, technologies and data structures.



IoTLEGO enables:

  • Consumers to use a large variety of devices in their IoT systems from different vendors or platforms.
  • Vendors of IoT devices to increase the number of ecosystems where their devices can be integrated.
  • Application developers to support a broad range of devices and platform without a need to develop vendor specific code.


Insight Centre for Data Analytics, The National University of Ireland Galway.




Researcher: Dr. Danh Le Phuoc


Commercial Contact:

Katrina Bradley

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