# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 2 Related Works  
Knowing the _PRR_ of neighbors could help sensor nodes to select the next-hop more effectively, which will improve network efficiency. To reduce the fluctuation of _PRR_, some studies use an exponentially weighted moving average (EWMA) to smooth _PRR_. [PERSON] et al. [3] proposed WMEWMA, which combines window averaging with EWMA for low pass filtering of _PRR_. [PERSON] et al. [4] proposed F-LQE (Fuzzy-LQE), which uses fuzzy logic to fuse four link parameters, namely smoothed _PRR_ filtered by WMEWMA, link stability factor, link asymmetry level, and averaged _SNR_. As F-LQE is too stable, [PERSON] et al. [5] and [PERSON] et al. [6] adjusted the link parameters involved in fuzzy logic respectively to achieve more agile and accurate estimations. Opt-FLQE (Optimized version of F-LQE) replaces the link stability factor in F-LQE with the smoothed required number of packet retransmissions [5]. ELQET (Enhanced LQE Technique) also uses four link parameters, namely _PRR_ obtained by LQI mapping, _SNR_ obtained by Kalman filtering, coefficient of variation of _PRR_, and averaged LQI, to characterize link quality [6]. [PERSON] et al. [7] proposed FaLQE, which realizes link adaptation by dynamically adjusting the smoothing factor according to the fluctuation of the link. Although these works have effectively improved the accuracy and stability, the inherent problem of _PRR_ is still not resolved: It always needs to take a long time to obtain an accurate _PRR_ estimation [8]. The mapping models between the communication distance and \(PRR\) can be obtained by counting the \(PRR\)s at different distances. Then, the communication distance could be used as an indirect link quality metric. [PERSON] et al. [9] classified the wireless link into three regions according to \(PRR\) values at different distances, which are connected region, transitional region, and disconnected region. Although the range of transitional region is the largest, links within this region are extremely unstable. [PERSON] et al. [10] found that the percentage of links in transitional region ranges from 5% to 60%, and typical characteristic of these links is bursty. [PERSON] et al. [11] established a mapping model between the communication distance and packet loss rate (PLR) by data fitting. [PERSON] et al. [12] proposed a mapping model between the communication distance and \(PRR\) by combining the theoretical \(PRR\) model and the log-normal path loss model. As \(RSSI\) and LQI can be obtained from the physical layer directly and are related to \(PRR\) closely, they have been widely used in LQEs. Popular radio transceivers used in WSNs, such as CC2420 [13] and AT86 RF230 [14] all provide \(RSSI\) and LQI measurements. By analyzing the correlations between \(RSSI\), LQI, and \(PRR\), [PERSON] et al. pointed out that \(RSSI\) is not a good discriminator of link categories, while LQI could effectively distinguish good, moderate, and bad links [15]. [PERSON] et al. also pointed out that the correlation coefficient of LQI and \(PRR\) is higher than that of \(RSSI\)[6]. By studying the relationship between LQI and PLR in outdoor environments, [PERSON] et al. [16] pointed out that there is a definite relationship between LQI and PLR. [PERSON] et al. [17] fitted a mapping model between LQI and \(PRR\) using the Cubic model. [PERSON] et al. [18] constructed a piecewise linear model of \(PRR\) as a function of averaged LQI. [PERSON] et al. [19] pointed out that only using LQI may overestimate the link quality under bad links. Meanwhile, there are also differences in the definition and implementation of LQI in different radio transceivers. [PERSON] et al. [20] constructed a mapping model between \(RSSI\) and \(PRR\) based on logistic regression. In the IEEE 802.15.4 standard, DSSS-OQPSK (Direct Sequence Spread Spectrum Offset-Quadrature Phase Shift Keying) modulation scheme is used in the 2.4 GHz physical layer.