# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Main Conclusions and Deep Analysis  
According to the analysis in Sections 5.1-5.4, environmental impacts on hardware-based LQEs in WSNs are summarized as follows:  
* **Conclusion 1.** It is not accurate to estimate _PRR_ using communication distance, and they may be useless when changing environments.
* **Conclusion 2.** When the environment changes, the fluctuation range of _RSSI_ and _SNR_ will be affected and that of LQI is almost unchanged.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Runway** & **Lawn** & **Corridor** \\ \hline Cubic model & 0.1146 & 0.0921 & 0.1386 \\ Piecewise linear model & 0.1152 & 0.0826 & 0.1499 \\ LR model & 0.1152 & 0.0897 & 0.1417 \\ \hline \end{tabular}
\end{table}
Table 6: RMSEs of LQI based mapping model in different environments.  
Figure 23: Effects of the piecewise linear model in different environments.  
Figure 24: Effects of the LR model in different environments.  
* **Conclusion 3.**\(RSSI\) based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently.
* **Conclusion 4.** The best environmental adaptability is gained by LQI and \(SNR\) based LQEs, as they are almost unaffected when the environment changes.  
Combined with Figures 7, 12 and 19, it can be seen that the fluctuation range of \(SNR\) and \(RSSI\) is significantly smaller than that of LQI with the same \(PRR\). This indicates that a larger window is needed to smooth LQI, which will inevitably affect the agility of LQI based LQEs. As can be seen from Figures 8, 15 and 21, when \(PRR\) changes from 20% to 80%, \(\mu_{lqi}\) corresponds to a range from about 70 to 100, while \(\mu_{snr}\) and \(\mu_{rssi}\) correspond to a range from about 1.8 dB to 4.5 dB and from about \(-95\) dBm to \(-91\) dBm, respectively. This indicates that using \(\mu_{lqi}\) to estimate \(PRR\) in the transitional region has a better resolution than \(\mu_{snr}\) and \(\mu_{rssi}\), which means higher accuracy in the transitional region. To verify this conclusion, RMSEs in the connected region, transitional region, and disconnected region for \(SNR\) and LQI based LQEs were plotted, as shown in Figures 25 and 26, respectively. It is shown that LQI based LQEs are really more accurate in the transitional region than \(SNR\) based LQEs, especially for the environments with higher fluctuation ranges of LQI and \(SNR\).  
why \(SNR\) based LQEs are more accurate than \(RSSI\) based ones because \(SNR\) considers the influence of noise and interference to some extent. On the other hand, each of the eight symbols is correlated with all 16 possible chip sequences, and the closest match is chosen for decoding. Then, LQI is calculated by the chip error rate with respect to the closest match [33]. In other words, LQI reflects the signal quality, not just the signal strength. Therefore, it has a better and more stable correlation with \(PRR\). Therefore, LQI based LQEs are more susceptible to changing environments than \(RSSI\) and \(SNR\) based ones.