Abstract
Accurate and agile link quality estimation is essential for wireless sensor networks. Using the mapping models between physical layer parameters and packet reception ratio, link quality can be estimated with advantages of high agility and low overhead. However, existing estimators based on physical layer parameters fail to utilize link quality information carried by different physical layer parameters efficiently and effectively and fail to effectively solve the problem that physical layer parameters fluctuate greatly, which makes them difficult to describe link conditions really. In this study, a lightweight, fluctuation insensitive multi-parameter fusion link quality estimator is proposed. Two physical layer parameters, Signal-to-Noise Ratio and Link Quality Indicator are preprocessed by exponential weighted Kalman filtering to get more stable estimation values. Then, these two parameters are fused using lightweight weighted Euclidean distance to fully utilize link quality information carried by them. On this basis, link quality is estimated quantitatively with the mapping model of the fused parameter and packet reception ratio, which is constructed by logistic regression. Experimental results show that the proposed estimator could reflect link quality more realistically. Compared with similar estimators, estimate error of the proposed one is reduced by 18.32% to 60.11% under moderate and bad links with large fluctuations, by 1.42% to 83.43% under sudden changed links, and by 16.64% to 65.61% under a long-time link. More importantly, computation overhead of the proposed estimator is equivalent to that of single-parameter estimators, but much less than other multi-parameter fusion estimators. Compared with the later, computation overhead is reduced by 72.36% to 95.61%.
Highlights
In recent years, wireless sensor networks (WSNs) have been increasingly deployed in many fields including military investigation, environmental monitoring, industrial control, home automation, and so on [1]
Existing estimators either take only one physical layer parameter into consideration, which makes them difficult to describe link conditions accurately, or employ too complicated multi-parameter fusion methods, which could not offer a good balance among accuracy, agility and low overhead
In order to make link quality estimators to offer a good balance among accuracy, stability and agility, this study proposed to preprocess physical layer parameters using exponential weighted Kalman filtering to obtain more stable and accurate estimation values
Summary
Wireless sensor networks (WSNs) have been increasingly deployed in many fields including military investigation, environmental monitoring, industrial control, home automation, and so on [1]. WNN-LQE employs wavelet neural network to predict SNR and its variance of the time, and estimates link quality quantitatively using the mapping model between SNR and PRR constructed by Gaussian probability density function [25] Such machine learning based methods did improve accuracy, it has great disadvantages of high computation overhead and poor efficiency. Compared with RSSI and LQI, Sm is more correlated with PRR and its variance is smaller These two methods provide a feasible way for lightweight fusion of physical layer parameters, there are some drawbacks in practice: Firstly, they were designed for link quality classification, but not for estimating link quality quantitatively.
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