# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 1 Introduction  
In the past few decades, wireless sensors networks (WSNs) have drawn much attention from academia and industry. WSNs are multi-hop self-organizing networks composed of hundreds and thousands of sensor nodes, which can monitor and collect various information from the deployed area in real-time. They have been successfully used in many fields, such as military surveillance, environmental monitoring, industrial control, and medical care [1]. Link quality estimation is crucial for WSNs due to their self-organizing characteristics as an effective estimation of link quality is the basis of high network performance. The packet reception rate (\(PRR\)) is the most direct metric for link quality. Although the \(PRR\) could reflect link quality directly, it always takes a long time to obtain an accurate estimation. Therefore, it is not sensitive to link changes and cannot adapt to the dynamic characteristics of the wireless channel in WSNs [2].  
In view of this, finding more agile link quality metrics has become a hot topic in the past years. Several studies have confirmed that certain mapping relationships exist between some link metrics and _PRR_, such as the Received Signal Strength Indicator (_RSSI_), Signal-to-Noise Ratio (_SNR_), and Link Quality Indicator (LQI). These metrics are easier to get than _PRR_ itself. Therefore, link quality could be estimated indirectly by constructing mapping models between these metrics and _PRR_, which improves the estimating agility greatly. Such approaches are often called hardware-based Link Quality Estimators (LQEs).  
However, the experimental environment in which the mapping model was obtained in most existing studies is relatively single, and the possible impacts of environmental changes are ignored. There are two important questions to be considered: 1. How does the change of environment affect the link metrics? 2. Can the mapping model be obtained in a specific environment be directly used in other environments? To answer these questions, this paper compares the performance of typical hardware-based LQEs in different environments. Then, the impacts of environmental changes on the applicability of these estimators are analyzed quantitatively. The results show that some link metrics will be affected when the environment changes. Depending on the metrics used, the hardware-based LQEs constructed in a specific environment may not be directly applicable to other environments. Consequently, it is necessary to fully consider the impacts of environmental changes in practice.  
The contributions of this study are as follows: (1) A comprehensive survey on the experimental environments and modeling methods in existing studies is presented. (2) Impacts of environmental changes on _RSSI_, _SNR_, and LQI are discussed. The results show that the fluctuation range of _RSSI_ and _SNR_ is more sensitive to environmental changes, while that of LQI is almost unaffected when the environment changes. (3) Aiming at the problematic _SNR_ calculation used in existing studies, a more reasonable calculation method is proposed. It is shown that with the proposed method, more accurate _PRR_ estimation could be made, especially when _SNR_ is low. (4) Impacts of environmental changes on typical hardware-based LQEs are analyzed. The results show that the environmental adaptability of hardware-based LQEs is completely different. LQI based LQEs is the least susceptible to changing environments.  
The rest of this paper is organized as follows. In Section 2, related works are given. This is followed by an experimental setup in Section 3. Section 4 summarizes typical hardware-based LQEs which are classified according to the link metrics they used, such as the _RSSI_, _SNR_, and LQI. The impacts of environmental changes on these LQEs are fully analyzed in Section 5. Finally, conclusions are presented and suggestions are made for future works.