In order to correctly alert people of possible transmissions, an algorithm must be able to process the given RSSI values and determine if the two devices were close enough.

This project addresses a crucial task in contact tracing: predicting if two devices are closer than 6 feet apart. Keywords: Contact Tracing, Bluetooth Devices, Machine Learning, COVID-19, Mobile App, Database, Privacy, Deep Neural Network, Google Tensorflow, Python Introduction Project Description It is also shown that there is a threshold value which can minimise the number of false positives. Therefore, this project aims to measure the direct distance in feet between two devices by mapping RSSI to distance using deep neural networks. However, due to the nature of the radiofrequency signal, fluctuations in the RSSI make it difficult to associate a measured RSSI with an exact distance. This proximity information can be used alongside timestamp data to estimate the distance between individuals over time.

This is because as the RSSI signal becomes stronger with the help of an rf amplifier, it indicates a closer proximity.

The Radio Signal Strength Indication (RSSI) obtained by Bluetooth can be used to estimate the proximity and duration of an individual’s exposure to patients diagnosed with COVID-19.

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