Intelligent Systems Data Ingestion and Analytics

This project will support the development of comprehensive, multidisciplinary Smart Building, Smart Transportation, and Smart City management systems in order to improve energy performance, operations cost, safety and reliability for large infrastructures in the private and public sector.
The research problem to be addressed is to develop effective methods to ingest and analyze massive amounts of streaming data from large numbers of WiFi-connected IoT devices monitoring buildings, vehicles, and transportation corridors within a Smart Campus or Smart City. This heterogeneous data comes in a wide variety of formats, at different sampling rates, in a variety of quantities. It must then be classified according to a strict ontology, stored in cloud-based databases, and recovered on demand for analysis and the computation of comprehensive key performance indicates for use by human operators and machine learning systems.
Our team has access to live streaming data both from real installations in labs and buildings and from Digital Twin simulators, with both IoT devices and Edge servers in the data mix, developed in partnership with academic researchers at partner institutions. FuseForward is working with Ryerson and other local partners to develop an IoT monitoring network on a college campus.

Faculty Supervisor:

Jiannan Wang


Peshotan Irani


Fuseforward Solutions Group Ltd.


Computer science




Simon Fraser University



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