Abstract
In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.
Highlights
Wireless Sensor Networks (WSNs) have been widely promoted over the last decade, to monitor various environmental parameters, e.g. temperature, light, and humidity
Based on the analysis above, it can be concluded that a better estimation of Signal to Noise Ratio (SNR) and the link quality margin can be achieved by using the information redundancy among Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), and the measured noise power Ne
In order to utilise the maximum capacity of WSNs in high-bandwidth and high noise applications, an accurate and low cost estimation of link quality must be available
Summary
Wireless Sensor Networks (WSNs) have been widely promoted over the last decade, to monitor various environmental parameters, e.g. temperature, light, and humidity. In order to improve the quality of the link estimation instead of using the SNR, some recently proposed methods represented by Vutukuru et al [5] make use of learning algorithms to estimate the link quality, which switch the radio transceiver into receiving state to monitor all the wireless activities and learn the potential interference levels from different devices These learning based methods require that the sensor node must be active all the time to overhear all wireless transmissions, which is not suitable for energy constrained WSNs. Other approaches calibrate the relationship between SNR and Packet Error Rate (PER) by exchange probe packets. As well as previously published results [2,16], have shown that the average LQI has a high correlation with the error performance This feature of IEEE 802.15.4 makes it possible to accurately estimate the link quality without the overhead of probe based calibration.
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