# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 2 Related Works
Therefore, \(PRR\) could be computed using the theoretical bit error rate model and \(SNR\), which can be calculated by subtracting background noise from \(RSSI\). For instance, [PERSON] et al. [21] and [PERSON] et al. [22] respectively use the theoretical model of DSSS-OQPSK for \(PRR\) estimation. When there are no co-channel interferences, the background noise usually remains stable for a few seconds or even minutes. As a result, changes in \(SNR\) with time are mainly caused by changes in \(RSSI\)[23]. On the other hand, some studies obtained the mapping relationships between \(SNR\) and \(PRR\) through data fitting. For instance, [PERSON] et al. [8] use a locally available \(SNR\)-\(PRR\) curve to estimate the \(PRR\). Some studies also use the combination of \(SNR\) and LQI to estimate the link quality. [PERSON] et al. [24] estimated the link quality by Effective-\(SNR\), which is produced by combining \(SNR\) and LQI with minimal additional overhead. [PERSON] et al. [25] proposed a lightweight multi-parameter fusion estimator, in which weighted Euclidean distance is used to fuse \(SNR\) and LQI effectively. Recently, machine learning algorithms began to be employed to optimize the mapping models, to improve the agility and accuracy of LQEs. [PERSON] et al. [26] proposed a machine learning-based scheme 4C, which uses a naive Bayes classifier, artificial neural network, and logistic regression to train historical data of \(RSSI\), \(SNR\), LQI and \(PRR\) offline and predicts \(PRR\) effectively. [PERSON] et al. [27] proposed a real-time link quality prediction model TALENT, which uses stochastic gradient descent online learning algorithm to train logistic regression classifiers using LQI and \(PRR\) values. [PERSON] et al. [28] took LQI as input and utilized a prediction game to construct an expert system model for link quality estimation. [PERSON] et al. [29] proposed RADIUS, a thresholding method based on Bayes theory, which uses mean value and variance of \(RSSI\) to identify the degradation of links, namely, from good links to bad links. [PERSON] et al. [30] proposed a link quality classification model, which fuses two physical layer parameters LQI and \(RSSI\) and trains the mean values of them by support vector machine. [PERSON] et al. [22] proposed WNN-LQE, which employs a wavelet neural network to predict \(SNR\) and its variance, and then estimates link quality quantitatively using the theoretical model between \(SNR\) and \(PRR\). Table 1 summarizes the main features of existing studies. It can be seen that most studies are conducted in a single environment. Although some studies [5; 7; 9; 10; 12; 20; 21; 23; 25; 26; 28] considered two or three different environments, they did not explore the impacts of environmental changes on the applicability of hardware-based LQEs. In fact, WSN applications may face a variety of deployment environments. Although existing studies have conducted an in-depth analysis of the relationships between communication distance, _RSSI_, LQI, and _PRR_, their results still cannot answer the two questions about environmental impacts presented in Section 1.