## 5 Evaluation and Results
### Discussion of Results  
The aim of this article is to design an energy-efficient light-weight protocol that can significantly limit the unnecessary transmission often associated with WSN deployments, with the main goal of minimizing the overall energy consumption in the network. From a previous work in [63], which examined the implication of packet transmission in a WSN that uses the W24 TH node prototype. The result is that one packet transmission suppressed is equivalent to approximately 13 min of added life-time for each node. Clearly, any data acquisition protocol that can limit packet transmission without missing useful events would be preferable. We analyzed SWIFNTET and the default adaptive prediction algorithms in this regard.  
From Figure 8a, Node 1 reveals the signal trace captured by SWIFTNET; when the fire event is generated and throughout the operation, 47 samples are collected in addition to the 288 samples that are acquired and compressed over an equivalent period of 24-hours at 5 min sampling interval. In total, 335 samples are logged on our CACTI database. Similarly, in Figure 9, Node 2 collected 223 samples during the fire event in addition to the 576 samples over a 48-hour period; this gives a total of 799 samples logged. From these figures, using SWIFTNET, Node 1 transmitted 33 packets out of the 335 packets and Node 2 transmitted 85 packets out of the 799 packets to the sink node for data gathering and processing.  
The same phenomenon using the default adaptive prediction algorithm as shown in Figure 8b and Figure 9b. From these figures, Node 1 transmitted 68 packets out of 335 packets and Node 2 transmitted 271 packets out of 799 packets respectively.  
In Figure 8c we show overlapping plots for both SWIFTNET and NLMS (default adaptive prediction) over a range of data points extrapolated from Figure 8a,b. The graphs reveal how the two methods are able to follow the evolution of the signal trace. Although, the signal trace looks less chaotic, SWIFTNET is still able to follow the evolution with the exhibition of few noises in the signal reconstruction. If the goal is to further improve the accuracy beyond 1% error bound, this generated signal trace from SWIFTNET could be smoothed out using extrapolation method. Overall, the prediction error for NLMS is within the user defined error bound \(\epsilon_{max}\). Similarly, the accuracy of SWIFTNET is not significantly different from the performance of the NLMS.  
By comparing the performance of SWIFTNET and the default adaptive prediction from the two Figures, from Node 1 SWIFTNET achieved (a) an \(R_{update}\approx\) 0.48, which fractionally represents an improvement of more than half, with RE of 0.0071 and (b) MAE of 0.2723 compared with the default adaptive prediction having RE of 0.0059 and MAE of 0.231. Although, the accuracy of the default adaptive prediction is slightly better than SWIFTNET, the default adaptive prediction transmitted more data to achieve that level of accuracy. We believe that the difference in accuracy is not significant compared to the amount of energy saved by using SWIFTNET.  
Similarly, from Node 2, SWIFTNET shown in Figure 9 achieved an \(R_{update}\approx\) 0.31, RE of 0.0071 and MAE of 0.271 compared with the default adaptive prediction having \(R_{update}\) of 1, RE of 0.0069 and MAE of 0.2717. Overall, both the energy saving and accuracy are in favor of SWIFTNET and comparatively, the difference in accuracy is not significant. Figure 11 and Table 1 show an average summary result comparing SWIFTNET with the default adaptive prediction method. The results are taken from 10 nodes chosen from our testbed. On average, SWIFTNET achieved more than 50% improvement over the default adaptive prediction method used without loss in accuracy. Both \(\Delta RE\) and \(\Delta MAE\) reflect accuracy loss or gain of SWIFTNET relative to the default adaptive prediction. A minus, _i.e._, \"\(-\)\" value, indicates SWIFTNET's loss in accuracy relative to the default adaptive prediction.