What is it about?
We fuse the digital twin representation of a building with its physical twin to explore complex data where it is generated. The Fused Twins allow to gather context information from the real space to better understand the digital data as a new form of immersive analytics. The cognitive load from understanding the data should be reduced by the spatial context.
Featured Image
Photo by Cosmin Serban on Unsplash
Why is it important?
The information density through the Internet of Things is increasing so quickly that we need paradigm to analyze the data and inform users. Fused Twins make information available where it originates and have a potential for democratized access to data as users of the space and learn what else is happening in the space.
Perspectives
I believe that linking digital twins and physical twins to Fused Twins will be a common occurrence in a few years. At this point, we can define these linkages and hopefully design them to be inclusive and serving a greater good. I hope with this article we can start the discussion on who owns the data generated by the Internet of Things and how we can use it to the betterment of humankind.
Jascha Grübel
ETH Zürich
Read the Original
This page is a summary of: Fused Twins: A Cognitive Approach to Augmented Reality Media Architecture, June 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3469410.3469435.
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Resources
Sensing and Predicting Human Behaviour in Public Spaces
Goal: The goal of this project is to use LoRaWAN-based sensor network to passively sense human behaviour in public spaces such as museums. The project contributes to build up the Internet of Things eventually enabling Smart Cities to work. The project will research how to gather, aggregate and interpret data that allows to derive human behaviour in public spaces without directly tracking human activity.
The Feasibility of Dense Indoor LoRaWAN Towards Passively Sensing Human Presence
Long Range Wide Area Network (LoRaWAN) has been advanced as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a Dense Indoor Sensor Network (DISN) with 390 sensor nodes and three gateways and empirically evaluated its performance for half a year. Our analysis of more than 14 million transmissions revealed that DISNs achieve a much lower distance coverage compared to previous research. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors such as concurrently received messages. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications. Our attenuation model indicates that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. We discuss the application of DISNs for the passive sensing and visualization of human presence using a Digital Twin (DT).
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