## 2 Related Work
### State of the Art of Hardware Metrics in the Estimation of Link Quality in Wireless Sensor Networks  
The RSSI of RF signals in WSNs is intended for various applications. The RSSI is a low-cost solution for distance estimation that replaces the global positioning system (GPS) in WSNs, which makes it a practical choice for localization schemes [11; 26]. The RSSI is also used as a hardware metric to estimate the link quality in wireless communication, as in [20], who used the RSSI in addition to the LQI for determining the link quality by using a Kalman filter and fuzzy logic. In [27], the RSSI and LQI were selected as estimation parameters to build a link quality estimation model based on support vector machine (SVM) with multi-class classification. In the free space, non-obstructive environment, the nature of electromagnetic wave propagation abruptly attenuates signal power near the transmitter and yields much less attenuation at further distances. This is described by the Friis Equation [28], which is directly derived from fundamental theory. \[P_{R}=\frac{P_{T}G_{T}G_{R}\lambda^{2}}{(4\pi d)^{2}} \tag{2}\]  
where \(P_{R}\) is the power at the receiving antenna; \(P_{T}\) is the output power of the transmitter; \(G_{T}\) is the transmitter's antenna gain; \(G_{R}\) is the receiver's antenna gain; \(\lambda=\frac{c}{f}\) is wavelength; \(d\) is the distance between the antennas, as shown in Figure 6.  
The Friis equation can be translated into numerous models. Because signal attenuation is usually linked to various environmental conditions, choosing the signal propagation model that is the most appropriate for the current environment is fundamental to ensuring the accuracy of signal power computation in a simulat  
Figure 6: Free space Friisequation parameters. is widely used to represent wireless signal propagation loss, which translates into the following equation:  
\[\left.P_{r}\right|_{dBm}(d)=\left.P_{r}\right|_{dBm}(d_{0})-10\right|_{dB}\times n \times log_{10}\bigg{(}\frac{d}{d_{0}}\bigg{)}+\left.X_{\sigma}\right|_{dB} \tag{3}\]  
where \(\left.P_{r}\right|_{dBm}(d)\) is the received signal power at the real distance; \(\left.P_{r}\right|_{dBm}(d_{0})\) is the received signal power at a distance of reference; \(d\) is the distance between the antennae; \(d_{0}\) is the reference distance between the antennae; \(n\) is the attenuation factor; \(\left.X_{\sigma}\right|_{dB}\) is the zero-mean Gaussian distributed random variable with a standard deviation of \(\sigma\). In most cases, RSSI-based ranging algorithms employ the shadowing model, which is nowadays used in localization-based algorithms, which use the trilateration method. This method needs the presence of three nonlinear reference points that are represented by three transmitters. A node applies RSSI measurements to approach its distances from the received packets [29]. In real circumstances, many external factors can attenuate the wave signals near the receiver, such as the reflection, diffraction, or absorption caused by a different type of obstacle; the physical environment such as wind, rain, temperature, lightning; and so on. WSN applications have been manipulated into various domains in indoor or outdoor scenarios. Different researchers have studied the impact of various environmental conditions on the LQI and RSSI. In [30], a study case was made to investigate the link quality and the received signal strength using the TelosB nodes of a WSN in a metal marine environment, where 18 nodes were distributed in a complex metallic environment composed of freight containers, engines, and different materials that cause the attenuation of signals. In [31], the impact of temperature on the RSSI and LQI were investigated in an oil refinery using CC2420 radio ships. The experimental results showed that temperature had a significant influence on signal strength and link quality.