## 3 Results
### Software Architecture  
All the drifters, both passive and active, share the same mechanical geometry and mass distribution (see Figure 2), despite having different features. This constraint enforces both types to experience the same passive drift. As local turbulence affects drifters to move apart, this could result in dispersion and fragmentation of the initial group of drifters into smaller \begin{table}
\begin{tabular}{l l} \hline \hline Optimized Sensing range & 0–14 \\ \hline Response Time & 95\% in 1 s \\ \hline Accuracy & +/ \(-\)0.002 \\ \hline Resolution & 0.001 \\ \hline Calibration & 1–3 points, -every year \\ \hline Data protocol & UART and I2C \\ \hline Operating voltage & 3.3–5.5 V \\ \hline Size (PCB/probe) & 13.97 mm \(\times\) 20.16 mm/12 mm \(\times\) 150.6 mm \\ \hline Weight (PCB/probe) & 1.76 g/49 g \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PH. Silver/silver chloride double junction PH probe connected to the EZO-RTD circuit with UART interface. Specifications are provided by the manufacturer. \begin{table}
\begin{tabular}{l l} \hline \hline Optimized Sensing range & \(-\)2000/ +2000 mV \\ \hline Response Time & 95\% in 1 s \\ \hline Accuracy & +/ \(-\)1 mV \\ \hline Resolution & 0.001 \\ \hline Calibration & Single point, -every year \\ \hline Data protocol & UART and I2C \\ \hline Operating voltage & 3.3–5.5 V \\ \hline Size (PCB/probe) & 13.97 mm \(\times\) 20.16 mm/12 mm \(\times\) 150.6 mm \\ \hline Weight (PCB/probe) & 1.86 g/49 g \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ORP. Silver/silver chloride double junction PH probe connected to the EZO-RTD Circuit with UART interface. Specifications are provided by the manufacturer. sub-groups. These may be far enough apart that they cannot communicate with each other via LPWAN. In this case, the initial cluster is no longer fully connected, i.e., it has separated into several smaller, independent sub-clusters. In this situation, the primaries, equipped with active thrusters, will play the role of maintaining external communication coverage with the fragmented clusters. In particular, during regular operation, every drifter will send sensor data measurements and its own geodetic position via radio communication. With this GPS data, the drifter primary will be able to assess the current position of all connected drifters, and thus estimate their actual kinematics. This process is called \"drifter tracking\" and it is fundamental for the next step named \"model predictive control\" (Figure 3). More in detail, during the \"drifter tracking\", information is used by each primary drifter to track all drifter positions and velocities on the basis of a kinematic model: assuming a constant irrotational current \(v_{c}\), the position \(p\) of each passive drifter can be simply modeled on the basis of the kinematic equation  
\[\dot{p}=v_{c}\]  
Figure 3: The blue rectangles are the tracker modules of the primary drifter that filter and estimate position and velocity. These values are passed to the blue square that represents the Model Predictive Controller (from MDM TEAM s.r.l.—MARTA project). Figure 2: MARTA Smart Drifters prototypes: secondaries on the left and primaries on the right. Note the trusters below the body of the primary type. The final design will have an appropriate shape to minimize direct wind drag. Calling \(\hat{x}=(\hat{\rho},\hat{\rho})^{T}\) the system of position and velocity estimations, we can predict its discrete evolution considering  
\[\dot{\hat{x}}_{k}=\left\{ \begin{array}{l}\dot{\hat{p}}_{k}=\hat{v}_{k-1}\\ \hat{v}_{k}=w_{k-1}\end{array} \right.\]  
with \(w\in N(0,Q)\) normally distributed white noise. The system is updated each time a measured position \(p_{k}\) is received, through a Luemberger-like filtering  
\[\hat{x}_{k}\leftarrow\hat{x}_{k}+K(p_{k}-\hat{p}_{k})\]  
with a \(K\) matrix of positive parameters to be tuned.