@@ -152,9 +152,9 @@ Finally we run a large number \todo{specify} of simulations with different trigg
Figure~\ref{fig:cycling-trade-off} shows the simulation results for the cycing tracking. The different colors of the points correspond to the different values for the triggering threshold of the sensor fusion algorithm. We can see that the trade-off is present and well controlled through the choice of the threshold. Furthermore two other interesting phenomena are pointed out by this simulation.
For low triggering values (red, green and purple points) there is less variance in terms of error since we are converging to the situation of the antenna being always turned on and therefore saturating the achievable tracking precision. Instead the variance in terms of energy consumption increases due to the sensor being more frequently turned on and off and threfore being affected by the random time required to fetch the satellites' signals.
First, for low triggering values (red, green and purple points) there is less variance in terms of error since we are converging to the situation of the antenna being always turned on and therefore saturating the achievable tracking precision. Instead the variance in terms of energy consumption increases due to the sensor being more frequently turned on and off and threfore being affected by the random time required to fetch the satellites' signals.
Looking at higher triggering values instead (blue, light blue and yelow points) opposite behavior is experienced. There is smaller variance in terms of power consumption since the antenna is turned on less frequently and there will be less uncertainty on how much time is overall spent while fetching the signal. Insterad the error becomes both lager and with higher variance due to the necessity of using more the IMU data that are less reliable.
Secondly, looking at higher triggering values instead (blue, light blue and yelow points) opposite behavior is experienced. There is smaller variance in terms of power consumption since the antenna is turned on less frequently and there will be less uncertainty on how much time is overall spent while fetching the signal. Insterad the error becomes both lager and with higher variance due to the necessity of using more the IMU data that are less reliable.
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@@ -165,7 +165,7 @@ Looking at higher triggering values instead (blue, light blue and yelow points)
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Finally figure~\ref{fig:car-trade-off} shows the simulations performed using the car data. Given the introduction of a varying number of visible satellites and therefore the possibility of losing GPS availability the possible behaviors of the system become much more various. Specifically they seem to spread radially away from the origin. This is reasonable since the loss of visibility will negatively affect both the accuracy, as the GPS data wont be available until a sufficient number of satellites become visible again, and the energy consumption, as the sensor will have to be turned on for relatively long time to re-aquire the ephemeris data. If, as shown in figure~\ref{fig:car-trade-off-zoom}, we zoom in the lower part of the plot we can see that the same behavior as the one described for the cycling data is evidenced.
Finally figure~\ref{fig:car-trade-off} shows the simulations performed using the car data. Given the introduction of a varying number of visible satellites and therefore the possibility of losing GPS availability the behavior of the system becomes much more various. Specifically they spread radially away from the origin. This is reasonable since the loss of visibility will negatively affect both the accuracy, as the GPS data wont be available until a sufficient number of satellites become visible again, and the energy consumption, as the sensor will have to be turned on for relatively long time to re-aquire the ephemeris data. If, as shown in figure~\ref{fig:car-trade-off-zoom}, we zoom in the lower part of the plot we can see that the same behavior as the one described for the cycling data is evidenced.
