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 pre-serving 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
COVID-19 & Privacy: Enhancing of Indoor Localization Architectures towards Effective Social Distancing