Citizen participation: crowd-sensed sustainable indoor location services
Ioannis Nasios Konstantinos Vogklis Avleen Malhi Anastasia Vayona Panos Chatziadam Vasilis Katos
In the present era of sustainable innovation, the circular economy paradigm
dictates the optimal use and exploitation of existing finite resources. At the
same time, the transition to smart infrastructures requires considerable
investment in capital, resources and people. In this work, we present a general
machine learning approach for offering indoor location awareness without the
need to invest in additional and specialised hardware. We explore use cases
where visitors equipped with their smart phone would interact with the
available WiFi infrastructure to estimate their location, since the indoor
requirement poses a limitation to standard GPS solutions. Results have shown
that the proposed approach achieves a less than 2m accuracy and the model is
resilient even in the case where a substantial number of BSSIDs are dropped.