In recent months we have seen how Bluetooth Low Energy has become, due to the epidemiological situation, the most used technology for contact tracing. With this in mind, the objective of this project is to test and contrast the robustness and functionalities offered by Bluetooth Low Energy for contact tracing. And overall to see the accuracy and capabilities it can offer. To study digital contact tracing techniques using sensing devices we will integrate Internet of Things elements into the Digital Contact Tracing architecture. We will not only evaluate the proximity between two people for their contagion, but we will also add other factors, such as air quality through Internet of Things network nodes. Secondly, we are trying to determine as reliably and accurately as possible, within the limitations of Bluetooth, the accuracy between the transmitter and the receiver. For this we will first present a new architecture for digital contact tracing, and some of its adaptations. Along with the adaptation of an Internet of Things node to add the ability to communicate via Bluetooth Low Energy and the advantages to be gained in this case. Along with these modifications we will try to apply a solution that does not represent a large increase in the cost of implementation. We will then evaluate the accuracy and reliability of Bluetooth Low Energy for determining distances between a transmitter and receiver. In addition to determining the contact risk between two people for the possibility of contact, we also evaluate the quality of the signal between contacts in several scenarios. With this we can evaluate how accurate is the communication through our Internet of Things node and other Bluetooth Low Energy elements. We will then evaluate the mechanisms and the accuracy we can obtain for determining the contact risk in two ways. First, we will try to calculate the risk based on the relationship between the signal strength and the distance between the transmitter and the receiver. Secondly, we will try to classify contact risk based on distance ranges and apply a Machine Learning classifier to determine the approximate distance between the transmitter and the receiver. With all this added information we will be able to evaluate and determine conclusively if BLE is a potential technology for digital contact tracing protocols. And also if adding Internet of Things elements to the Digital Contact tracing architecture is an option to improve the determination of the contagion risk
