Machine intelligence techniques for smart and sustainable planning and operation of IoT and Edge computing applications (MIRAI)

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Distributed AI, secure analytics, access control, privacy

Project description 

Only 1% of the data generated by end nodes and available at the edges of modern networks is utilised; the rest is neglected due to limitations such as low bandwidth and high latency in the connection to the cloud, and poor security/privacy standards. The current approach for IoT is to leverage cloud infrastructures to address constraints at the end/edge nodes, but this is no longer viable due to hard real-time requirements of (mission-) critical applications, increasing AI usage and high demands on storage and computational power.

As a decentralised intelligence framework, MIRAI will enable the optimal distribution of AI computing tasks and workloads across existing computing nodes, serving as a truly scalable edge computing software toolkit for IoT and edge computing applications. Through the MIRAI Framework Building Blocks (MFBB), appropriately sized AI modules will be deployed at nearby available edge nodes. This will provide a low-latency distributed ecosystem for AI-enabled computing in IoT. With application services and tasks deployed on local resources, network problems will become less critical. This decentralised approach will make the MIRAI solution more robust (by enabling new failover mechanisms) and secure (as the computations are executed directly on the source without the need to move the data around).

In Belgium, the MIRAI solution will be applied in the domains of distributed renewable energy systems, traffic management and water management. 

Project partners (BE) 

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Project details

December 2020 - November 2023

With the support of

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Projet subsidié par la Region de Bruxelles-Capital - Innoviris/project gesubsidieerd door het Brussels Hoofdstedelijk (Project subsidized by the Brussels Capital Region - Innoviris):

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This is a cross-domain project in collaboration with the software engineering programme of Sirris.