As a software research & development company, we are constantly seeking to discover new and unfulfilled demands in the Indoor Positioning realm to create unique and scalable solutions for them. Now we have been granted substantial funding for our latest project focusing on Building Utilization Intelligence (BUI).

Building Utilization: New needs discovered

Indoor Positioning and Navigation services have been around for a few years now and  unbounded creativity has led to an overwhelming amount and diversity of use cases.

Now that this technology has become accepted and approved of across verticals such as transport, retail and healthcare etc., voices demanding further features and added value have been raised.

By 2018, experts predict around $10 billion in spending to be touched or directly affected by indoor localisation. In detail, ABI research predicted in 2013 that the indoor location and positioning part of this growth will be about $4 billion. This development is driven by increased demand for indoor mapping, indoor analytics and the strive to close the gap between online and offline retail.

High utility and competitive dynamics will propel the expansion of Indoor Positioning services and analytical data forward, making it indispensable for all industries’ players who want to stay competitive.

What’s BUI?

With Building Utilization Intelligence, indoo.rs endeavours to meet these arising needs. BUI means the comprehension of how visitors

  • move through a building,
  • interact with its facilities,
  • are affected by their environment, e.g. weather and
  • influence each other, e.g. as part of a crowd.

This knowledge about visitor behavior and mobility aids venue operators in optimizing building utilization for more efficient and sustainable use of resources in the buildings, a higher degree of accessibility to users, and increased profitability.

In order to deliver this knowledge, sufficient amounts of high-quality data about building usage need to be acquired, features and information need to be mined, and finally, the data has to be presented for interpretation. It is important to deliver on all three areas – acquisition, data mining, and analytics -, as they highly depend on each other.

Therefore, indoo.rs together with partners from the Vienna University of Technology is working on innovations in these three major areas.

The innovation for acquisition entails mathematical models for reliability estimation, in order to increase the amount and quality of movement data, and thus we expect higher accuracy of predictions later in the pipeline. However, we also work on the integration of other building data, e.g. sensors and GIS data.

For data mining, machine-learning-based methods are developed and one of the results from this step will be a mobility model, which is an abstract description of how people (can) move through the building and how the environment affects the visitors’ movement.

As some of these concepts are fairly new and might be too complex to derive insights from directly, we work simultaneously on visualization methods specifically tailored to help understand the data mining results. This entails exploration of historical data (e.g. “What are the most common routes on a Monday when it is raining?”), real-time event detection, status inquiry and monitoring (e.g. “Notify me on drastic changes in visitor count or movement!”) and prediction through simulation (e.g. “If this passage is blocked, how will users choose to walk?”).

The funding

The Austrian Research Promotion Agency is a state owned organisation, whose mission is to identify and financially support pioneers of innovative project ideas in the field of applied and industrial research.

The agency has identified the need and use for the indoo.rs BUI solution and decided to support it with a funding of around 70% for the total 1 million EUR project costs. The research project was kicked off in July 2017 and  is expected to run two consecutive years.

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