# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 6 Conclusions and Future Works  
To satisfy the performance requirements of WSNs, agile, and accurate link quality estimation is necessary. To achieve this goal, hardware-based LQEs are usually employed which depend on mapping models between \(PRR\) and some indirect metrics such as the \(SNR\), \(RSSI\), and LQI. However, existing studies did not consider the impacts of environmental changes on the applicability of these estimators. To solve this problem, three different environments are chosen in this paper, and environmental impacts on typical hardware-based LQEs are analyzed quantitatively.  
The experimental results expose that the traditional calculation method of \(SNR\) used in existing studies is problematic. The transceiver only measures the power at the antenna without attempting to distinguish whether it is due to signal or noise. Therefore, the noise power also contributes to the \(RSSI\) value, which makes the actual \(SNR\) is nonlinear with \(RSSI\) and noise power, especially when \(SNR\) is small. For this problem, a more reasonable calculation method is proposed. It is shown that after correction, the \(SNR\) based theoretical model is more accurate, which makes the LQEs based on this model much more valuable.  
It is not accurate to estimate \(PRR\) using communication distance and may be useless when the environment changes. The performance of \(RSSI\) based LQEs may degrade when the environment changes. Fortunately, it could be compensated conveniently by measuring the background noise or carrying out normalization to \(RSSI\). The best environmental adaptability is gained by LQI and \(SNR\) based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than \(SNR\) based ones in the transitional region. On the other hand, when the environment changes, the fluctuation range of \(RSSI\) and \(SNR\) will be affected and that of LQI is almost unchanged. However, compared with \(SNR\) and \(RSSI\), the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.  
In the future, the impacts of node configurations (in other words, heterogeneous nodes) on LQEs will be explored to gain a deeper understanding, including the antenna height, frequency channel, transmit power, antenna angle, and others.  
Conceptualization, W.L. and R.L.; methodology, W.L. and Y.X.; experiments, Y.X., J.X., and D.Z.; data analysis, W.L., Y.X., J.X., and D.Z.; writing--original draft preparation, W.L. and Y.X.; writing--review and editing, W.L., R.L., and S.H.; visualization, Y.X. and D.Z.; project administration, W.L.; funding acquisition, W.L., D.Z., and R.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant No. 61601069, Chongqing Research Program of Basic Research and Frontier Technology under Grant No. cstc2017 jcyjAX0254 and cstc2016 jcyjA0515. The authors declare no conflict of interest.