# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 2 Related Work  
Machine monitoring plays an important role in the industry due to its effects on production quality and production cost. It can be divided into two types, machine condition monitoring and machine process monitoring. The first one refers to the monitoring of machine components, such as gears and bearings, and the second one refers to monitoring workpieces and tools. The first step in machine monitoring is signal acquisition, for example: vibration, temperature, cutting force, product labelling, and others. In this context, WSNs are the key to achieving advanced automated systems by turning traditional machines into modern machines. The synergy between WSN and machine learning allows intelligent decision making and LQE estimation with efficient computational time and high accuracy [23]. The propagation environment of radio frequency signals can change in space and time and affect the quality of the link in WSN. Consequently, to achieve reliable and error-free transmission, the transceiver's parameters must be adjusted depending on the best LQE estimate. Over the last few years, machine learning algorithms have been used to estimate LQE due to their ability to process and learn from large amounts of data, which can be gathered from various scenarios and technologies [24]. The link quality metrics for LQE algorithms fall mainly into three categories: software, hardware, and scores [25]. The software LQE metrics such as packet reception rate (PRR), required number of packet retransmissions (RNP), and expected transmission count (EXT) are calculated using the transmission variables. The PRR measurement is the ratio of the number of packets successfully received to the number of packets transmitted within a time window, but PRR-based algorithms are not sensitive to rapid fluctuations in link quality [26]. The RNP measurement is the average number of packet transmissions and retransmissions required before a successful reception; nonetheless, it is unstable and it cannot estimate packet delivery for asymmetric links successfully [27]. The EXT measurement refers to the number of transmissions required to send a complete packet; however, its implementation often fails in congested networks [28]. The hardware LQE metrics such as received signal strength indicator (RSSI), link quality indicator (LQI), and signal to noise ratio (SNR) are acquired from the transceivers. The RSSI is the measurement of the intensity of the signal present in the receiver that evaluates the quality of the link via supervised learning algorithms, and it classifies the quality of a link as high or low for a given routing protocol. However, the RSSI indicates only the power of the received signal at the receiver, and it does not provide an accurate measure that considers the affectation due to noise. The LQI measurement is a characterization of the strength or quality of a received packet according to the IEEE 802.15.4 standard; however, the algorithms based on LQI have three disadvantages:  
1. The variance of the LQI increases significantly as the quality of the link degrades;
2. There is no consensus among transceiver manufacturers on how to calculate LQI;
3. It is only useful for transceivers that work with the IEEE 802.15.4 standard [29]. The SNR metric is the difference, in decibels, between the received signal strength and the floor noise. Despite algorithms based on SNR measurement being easy to implement,these are not suitable for random SNR behaviors, and these are unable to provide an accurate description of link quality [30]. The score algorithms to calculate the LQE metric use various input parameters; for example, [PERSON] et al. [31] used SNR, LQI, and RSSI of up and down links in a neural network architecture of seven stacked layers to extract information from the asymmetric characteristics of the input data. Then, the classification was made by an SVM algorithm. This algorithm is focused for bidirectional communications and it is not suitable for unidirectional communications, such as the application presented here.