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The Occupancy Monitoring System: IoT Sensors and Gateways resulting in a Delivery Platform

Written by Anju Kakkar

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In partnership with Embedded Sense, Inc. (ESI), Humber researchers have successfully built a dashboard and IoT sensor interface, creating a more granular view of the sensor data being collected, with the ability to create a time-based random forest model for predictive capability on occupancy of areas. 

A graphic image of a building with the windows lit up in a "?" pattern to symbolize the more recently prevalent issue of facility capacity usage and how we monitor it. The graphic includes the quote "We are helping companies solve real problems."

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With the advent of a mobile workforce and space sharing initiatives, it has become increasingly difficult to plan occupancy and estimate accommodation rates and usage (Guo et al., 2010). The introduction of IoT and Big Data Analytics can alleviate this problem by a comprehensive approach with data-driven solutions to monitor the changing occupancy landscape and provide efficiencies in the process. (Breur, 2015).

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Industry Partnership

Embedded Sense, Inc. (ESIis a premier technology partner specializing in leveraging embedded processing, interface, sensing and wireless expertise into product solutions for global customers in the military, industrial/ commercial and medical markets. ESI provides a full suite of intellectual property management, proof of concept, engineering design, product prototyping and commercialization services.

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“If you have internal product development or manufacturing resources, but are lacking key skills or resource depth, ESI is the ideal partner – as an “on-demand” external resource, ESI adds depth to your current project development team without the lengthy and difficult process of budgeting for and sourcing new hires.” – ESI

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Research Objective:

The Occupancy Monitoring System is a smart building technology product of ESI. The system’s first requirement was to monitor the utilization of workspaces and help optimize the workspace to reduce costs. The second requirement of the system was to provide alerts based on a criterion of different forms of activity or inactivity at specified intervals. A dashboard and integration engine were built to work with ESI’s sensors which collected the data from various access points.

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With this solution, ESI will have the ability to efficiently inform decision-making customers across a geographically disbursed real estate portfolio with current occupancy, capacity, vacancy and density metrics. The solution will allow customers to gain a true measure of occupant demand, including mobile workers and contractors, allowing businesses to develop a long-term real estate strategy in confidence, knowing that supply and demand trends can be checked against the plan. The solution is a combination of IoT and Workspace Monitoring System (WMS).

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The cost-effective solution consists of interrelated cycles between embedded sensors, communications and cloud-based IoT analysis dashboard, which will enable businesses and organizations to:

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(1) Define workplace strategy aligned with organizations goals and user needs and expectations
(2) Optimize the use of workspace to reduce costs
(3) Effective management of real estate (exponential grow prediction) and
(4) Gather historical data to provide valuable insights.

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Dr. Ginger Grant, Ph.D., Dean of Humber Research & Innovation, “We are helping companies solve real problems. And, at the same time, our students get exposed to real-world challenges and work-integrated learning.

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Humber successfully acquired the College and Community Innovation Program – Engage Grants for Colleges, entitled “Occupancy Management Solution”.

In building the dashboard and IoT sensor interface, the research team changed the data storage model, which enabled creating a more granular view of the sensor data being collected, generating the ability to create a time-based random forest model for predictive capability on occupancy of areas. Power consumption was a key feature of the proposed system.

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Humber Researchers pointed out, “A future recommendation would be to enhance this model overtime to predict with a finer degree of accuracy space utilization by day, week and month. Future phases of research can include running a pilot project to establish operational efficiency as well as enhancing the predictive capability of space utilization by engaging with more Machine Learning Algorithms.”

System components:


(1) Sensors: The system uses specially built self-sufficient occupancy sensors from ESI. These sensors monitor: Occupancy, Temperature, Movement (vibration).

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(2) IoT Gateways: IoT Gateway monitors data collected from several sensors and transmits the data to the cloud application.

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(3) IoT Dashboard: The database has been designed as a multi-tenant database. Mongo-db and node.js are the initial software implementation recommendations, while Power BI engine will be used for analytics.

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Team Leads:


The Humber Research lead was Prof. Orren Johnson, Faculty of Applied Sciences and Technology (FAST), as the Principal Investigator (PI), assisted by Prof. Mihai Albu, Ph.D., FAST as the Co-investigator for the project. Prof. Orren provided guidance to Research Assistants and worked with the Co-investigator and the industry partner to accomplish the milestones identified in the project plan. He is a Big Data professor and has vast real-life expertise in technology, research and innovation and is an Advisory Board Member at Humber. Dr. Mihai’s areas of expertise span data analytics to AR/VR, algorithm development and adaptive analytics.

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Two students were hired as applied research assistants from Humber’s Information Technology Solutions program for the project’s duration. Working with the industry partner, the PI, and the Co-Investigator, these Research Assistants were the Software Developers, designing, developing and implementing the solution.

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Frank Gerlach, CEO, ESI, worked with the project team for the duration of the project to accomplish the milestones identified in the Project Plan.

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Dr. Ginger Grant, Ph.D., Dean of Research & Innovation,  provided support and was responsible for the operational and financial administration of the grant.

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Potential for Impact:


The academic institution gained specific practical implementation knowledge in IoT and Big Data disciplines solidifying processes knowledge. The partner benefitted from streamlining their IoT sensor development and real-world adoption. The private sector partner will further create innovation and business leadership in the smart building management industry space. The students were able to experience a real-world software application build-out process and learn project management, machine learning and utilizing new technologies.

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Benefit to Canada:


In the context of fast-evolving mobile technology and space sharing environments taking shape in Canada and globally, this solution will prove a competitive advantage for the Canadian industry, not only in occupancy management but also in the broader “smart building” and “smart city” initiatives. The economic goals are to provide a service that maximizes space and energy for many brick and mortar organizations. By managing space and energy efficiently, the system usability has an immediate impact on both social and environmental arenas by measurably reducing carbon footprints by cost and usage.

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“The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so”, says Kevin Ashton, a visionary technologist.

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The success of projects such as these is an outcome we are enthusiastic about sharing with our audience.

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In the meantime, we want to hear from you. What are some of the projects you are leading? What’s in the pipeline for your organization? Leave a comment below.

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To get involved, visit:https://www.humber.ca/research/get-involved/