Journal article  Open Access

COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing

Barsocchi P., Calabrò A., Crivello A., Daoudagh S., Furfari F., Girolami M., Marchetti E.

COVID-19  Privacy  Social distancing  Indoor localization system 

The way people access services in indoor environments has dramatically changed in the last year. The countermeasures to the COVID-19 pandemic imposed a disruptive requirement, namely preserving social distance among people in indoor environments. We explore in this work the possibility of adopting the indoor localization technologies to measure the distance among users in indoor environments. We discuss how information about people's contacts collected can be exploited during three stages: before, during, and after people access a service. We present a reference architecture for an Indoor Localization System (ILS), and we illustrate three representative use-cases. We derive some architectural requirements, and we discuss some issues that concretely cope with the real installation of an ILS in real-world settings. In particular, we explore the privacy and trust reputation of an ILS, the discovery phase, and the deployment of the ILS in real-world settings. We finally present an evaluation framework for assessing the performance of the architecture proposed.

Source: Array 9 (2021). doi:10.1016/j.array.2020.100051

Publisher: Elsevier

Back to previous page
Projects (via OpenAIRE)

Cyber Security Network of Competence Centres for Europe

BibTeX entry
	title = {COVID-19 & privacy: Enhancing of indoor localization architectures towards effective social distancing},
	author = {Barsocchi P. and Calabrò A. and Crivello A. and Daoudagh S. and Furfari F. and Girolami M. and Marchetti E.},
	publisher = {Elsevier},
	doi = {10.1016/j.array.2020.100051},
	journal = {Array},
	volume = {9},
	year = {2021}