## 5 System-Level Performance Evaluation
### Evaluation Results
#### 5.2.1 Ecg_UoP  
Based on Equations (2)-(5) and the individual components' characteristics as presented in previous section, the analytical results are presented in Tables 6 and 7.  
As it can be easily extracted, significant benefits can be offered by integrating the proposed compression module into a WSN node. What is interesting to note is that when increasing the frequency clock, although it leads to increased power for the compression module, it also leads to performance enhancements in both metrics considered i.e., end-to-end delay and power consumption. This is attributed to the considerably faster operation of the module, which decreases drastically the time period required by the module to be active. However, this performance enhancement also seems to have an upper limit, since increasing the frequency beyond 20 MHz it leads to negligible performance enhancement. Thus, the respective increase to power requirements beyond 20 MHz clock frequency is effectively wasted since it does not lead to further performance enhancement.  
Finally, it is important to emphasize the fact that a reduction of almost 60% can be calculated both for the delay and the energy dissipation using the proposed hardware compression module.  
Focusing on IEEE 802.15.4-based measurements, what is especially important in this case is that a drastic enhancement is apparent even with the lowest clock frequency considered (i.e., only 1 MHz). This is attributed to the much longer delay required to transmit data compared to the BT case. Consequently, in this case, it proves significantly beneficial to compress data before wireless transmission. This is indicated both delay and energy wise, since the compression phase, due to its hardware implementation, leads to drastically low delay operation, which in turn minimizes any end-to-end delay and energy overhead. Furthermore, the same pattern is identified in the graphs indicating  
Figure 8 reveals behavioral patterns similar to the ones in previous cases, strengthening the indications regarding the performance enhancement of the compression module. Focusing on BT-based graph, an interesting point is the fact that in comparison to the respective measurements considering ECG UoP signal, in this case a steadily higher performance enhancement (\(-8\%\)) can be extracted. This enhancement increase is attributed to the lower sampling frequency of the EkgMove sensor leading to fewer samples for same time interval, thus leading significantly faster compression execution from the proposed compression module. This is actually the part that effectively saves network resources either in the delay or in energy consumption domain. Also, in this case the type of signal has a critical role; this is due to the fact that the more compression prone the signal is the higher the performance enhancement will be, which in this case is close to \(70\%\) both for delay and energy reduction.  
Focusing on 802.15.4-based graphs, once again the exhibited delay and energy consumption decrease are both quite emphatic. Both reach up to \(70\%\), which can be achieved even with a moderate FPGA clock frequency of 20 MHz (62.5 MHz being the highest supported by the FPGA development board considered for the implementation).  
Another interesting observation concerns the fact that even considering the significantly lower power consumption of IEEE 802.15.4 transceivers (compared to Bluetooth counterpart), the integration of a compression accelerator module would still lead to a significant performance boost and consequent enhance the efficiency of any decision support system.