Abstract
With the rise of COVID-19, the sustainability of air transport is a major challenge, as there is limited space in aircraft cabins, resulting in a higher risk of virus transmission. In order to detect possible chains of infection, technology-supported apps are used for social distancing. These COVID-19 applications are based on the display of the received signal strength for distance estimation, which is strongly influenced by the spreading environment due to the signal multipath reception. Therefore, we evaluate the applicability of technology-based social distancing methods in an aircraft cabin environment using a radio propagation simulation based on a three-dimensional aircraft model. We demonstrate the susceptibility to errors of the conventional COVID-19 distance estimation, which can lead to large errors in the determination of distances and to the impracticability of traditional tracing approaches during passenger boarding/deboarding. In the context of the future connected cabin, a robust distance measurement must be implemented to ensure safe travel. Finally, our results can be transferred to similar fields of application, e.g., trains or public transport.
Highlights
The current COVID-19 pandemic has shown that global travel activities are a key factor for hardly or even non-traceable infection chains
Future concepts will be enabled by a smart sensor environment, which demands for precise position information
We have argued that commonly utilized Receiver Signal Strength Indicator (RSSI) measurements can lead to false-positive and false-negative encounter classification, depending on the path-loss model tuning, lowering the reliability and user acceptance of technology-aided social distancing options
Summary
The current COVID-19 pandemic has shown that global travel activities are a key factor for hardly or even non-traceable infection chains. Technology-aided approaches are applied, especially in the form of digital social distancing and contact tracing frameworks In this context, contact tracing refers to surveying the proximity between users and tracing critical encounters in case of positive coronavirus reports [9]. The influence of environmental conditions is further increased in areas with limited space, as well as concave and metallic surroundings, such as aircraft cabins These environments naturally provide demanding conditions for wireless signals, due to possible reflections, scattering, and attenuation of transmitted signals. We discuss and analyze the challenges of RSSI based distance estimation in challenging environments and propose the utilization of further signal measurands to increase technology-aided social distancing performance
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