We implicitly assumed that if we didn’t move the phones or interfered directly with the signal, the strength would be constant. RSSI variabilityĪll of the above was based on a fairly static view of the strength of the signal. We haven’t seen any public data about that side though, at the moment. Similar variation is expected to be present on the receiving side, and to need similar calibration. This point might be concerning due to differential impacts that might result from this, as iPhone ownership correlates with many socio-economic indicators. the strength is not consistent at all, there is huge variability for the same device (will get to this more below).However, there are two concerns jumping out from this graph: Indeed, this is what the Apple/Google protocol does, at least to some extent (see below). Indeed, when sending its beacons, a phone could also send as metadata its emission strength so the receiving phone can figure out accurately the difference. The TraceTogether team (and indeed others) are doing these measurements so they can calibrate the devices. Testing of different devices emission strength, in a highly consistent setup (same receiver, interference-free room). What effect might the body of the wearer or their hand have on the signal strength and hence the computed distance. The most natural type of interference to expect with smartphones is the human body. We will now discuss the following points:įlight path interference Body interference In particular it could be that the signal comes from/goes in a direction that is more shielded to the antenna due to other components, so we can’t really fold this effect into the emission power/receiver sensitivity effects, as it is directional. The antenna path effects are due to path the signal has to take within the phones before getting out/after getting out. The middle one is tied to what happens between the two. The first two are tied to the emitter, the second two are tied to the receiver. There are many factors that can affect the received signal strength: Alternatively, you can use the rule of thumb that 6 units of RSSI multiply the length estimate by a bit more than 2 (the scale is logarithmic on the RSSI side). From the formula above, we see that a difference of 20 units (measured in dBm) should lead to a distance estimate multiplied by 10. It is therefore dependent on the measuring device, but the difference between such measurements can be productively compared, if taken from the same device. Here, we will use RSSI (“received signal strength indication”), which uses arbitrary units. The units for measuring signal intensity are a bit wonky. ISTUMBLER ANDROID REVIEW BLUETOOTHSource: Why use Bluetooth for contact tracing? The inverse square law, translating RSSI differences into multiplicative factors for distance. Free from interference, the signal intensity should decrease like the square of the distance (the Power Loss Exponent would be different indoors). The idea is here to use this attenuation of the signal to infer distance. If you are far away from an electromagnetic signal, it will lose strength. The public health recommendation for distance is that the COVID-19 risk decreases beyond 2m. Exposure is calculated as a function of time and distance to an infected person. The goal of COVID-19 contact tracing apps is to calculate a risk of infection, based on exposure, and to notify users at risk. We review evidence coming from the developing teams of what might or might not work to deduce distance from Bluetooth signal strength, and eventually question whether this is truly the goal of all those who implement contact tracing apps. The distance is inferred from Bluetooth signal strength, which is a step that has not been empirically tested properly. Inferring distance from Bluetooth signal strength: a deep diveĬontact tracing apps intend to predict exposure to a COVID-19 infection, where exposure is computed as some function of time and distance to an infected person.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |