## 4 Use Case: Application Scenario
### SWIFTNET Design Illustration for a Wildfire Application  
Figure 4 illustrates SWIFTNET design strategy. The points of interest between \(50\) to \(400\) sample points are shown with the dotted lines. This figure shows an air temperature data trace from a wildfire monitoring application. To illustrate, the diagram is partitioned into two zones of interest:  
1. **The Normal Zone**: In this zone, since the temperature is below the threat level or potential fire hazard, a low sampling interval would suffice. In SWIFTNET, this zone is controlled by the CS algorithm. 2. **The Fire Zone**: In this zone, locations A and B are the major points of interest in the event of a combat operation during a wildfire disaster. A higher sampling interval and an aggressive data collection would be necessary, the more information gathered during these periods, the better the operation is likely to be. Similarly, the longer the network lifetime achieved during the fire incident, the better the inference that would be generated. Consequently, we strive for high quality information reporting and longer lifetime deployment. To achieve our goal in SWIFTNET, this zone is automatically controlled by our adaptive prediction algorithm. From Figure 4, the emergency threshold \(\rho\) is introduced to dynamically switch between CS and the adaptive prediction algorithm. This threshold temperature can be set by the users, depending on the past history of wildfire events, or it could be learned from the historic data if necessary. When the air temperature is below \(\rho\), _i.e._, in the Normal zone, the CS algorithm is invoked in SWIFTNET, which samples the environment at a fixed sample interval \(\beta\). In our experiments, we set \(\beta\) to 5 min, which amounts to 288 samples in a 24-hour period. The sparsity \(K\) is set to 25%, which is 72 non-zero samples collected and transmitted in one packet every 24-hour period. Similarly, when the algorithm switches to prediction, the prediction phase uses a user defined error bound \(\epsilon_{max}\), which we set to \(\pm\)0.3 \({}^{\circ}\)C _i.e._, \(<\)1% error margin. The error bound controls whether data is transmitted or suppressed. In addition, it contributes to the rate of reactivity of the network. Recall from Equation (15) that an increasing \(X_{sense}\) automatically increases the sampling interval, and vice versa. With this design, the prediction algorithm is adaptive and highly responsive to changes in the air temperature signal. Similarly, in order to prevent false alarms, SWIFTNET uses a buffer window size \(n\) in the prediction model. As mentioned previously, once the threshold \(\rho\) is reached, and if the user error bound \(\epsilon_{max}\) condition is met within the computed \(\gamma\) sampling interval, an alarm is automatically invoked and data transmission resumes. However, if this condition is not met, the prediction phase does not start at the sensor nodes until the window size \(n\) is full, which is then transmitted to the sink node without invoking a fire alarm. For example, in Figure 4 we note the few spikes between 90 to 100 sample points, where the temperature rises above the \(\rho\) value, but quickly drops below the emergency zone, hence, no alarm is triggered due to the reasons aforementioned. Apart from the reactivity of the network being controlled by \(X_{sense}\), decreasing the user error bound equally increases the reactiveness of the network and vice versa. The additional advantage of SWIFTNET is that, it requires no prior knowledge of the monitored environment. The algorithm is light-weight and can be practically implemented on any existing 32-bit node platform that is currently available in the market. Figure 4: SWIFTNET: design illustration. In summary, since the majority of the time the air temperature could be significantly lower than the threat level, by using CS for the data gathering during this period, significant savings on energy resources are achieved without a significant loss in accuracy in the measured values.