A Hybrid Reliable Routing Algorithm Based on LQI and PRR in Industrial Wireless Networks

In Industrial Wireless Networks (IWNs), the communication through Machine-to-Machine (M2M) is often affected by the noise in the industrial environment, which leads to the decline of communication reliability. In this paper, we investigate how to improve route stability through M2M in an industrial environment. We first compare different link quality estimations, such as Signal-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Ratio (PRR), and Expected Transmission Count (ETX). We then propose a link quality estimation combining LQI and PRR. Finally, we propose a Hybrid Link Quality Estimation-Based Reliable Routing (HLQEBRR) algorithm for IWNs, with the object of maximizing link stability. In addition, HLQEBRR provides a recovery mechanism to detect node failure, which improves the speed and accuracy of node recovery. OMNeT++-based simulation results demonstrate that our HLQEBRR algorithm significantly outperforms the Collection Tree Protocol (CTP) algorithm in terms of end-to-end transmission delay and packet loss ratio, and the HLQEBRR algorithm achieves higher reliability at a small additional cost.

Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks

Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.

Link Quality Assessment Algorithm for Heterogeneous Self-organizing Maritime Communications Network

The article introduces a method of performing a radio link quality assessment based on the Link Quality Indicator (LQI) which will be calculated for every system that is available. The method presented has been developed during the netBaltic project completed in Poland and generally applies to the so-called maritime zone A, i.e. the sea area where ships are still within the range of shore-based radio communication systems, particularly 3G/LTE cellular networks. The algorithm was developed based on the results of measurements obtained during two separate campaigns. That measurement data served as a basis for the method’s initial assumptions and was utilized during the method’s verification.

A Link Evaluation Method Employing Statistical Means of Received Signal Strength Indicator and Link Quality Indicator for Wireless Sensor Networks

This paper is concerned with a wireless receiving link evaluation method using statistical means of received signal strength indicator (RSSI) and link quality indicator (LQI) based on the IEEE 802.15.4 protocol for wireless sensor networks. Traditional methods using single RSSI and single LQI based on the IEEE 802.11 protocol have the disadvantage of the inaccurate evaluation. In this paper, we carry out a quantitative emulation experiment via computing statistical means of RSSI and LQI based on wireless sensor networks protocol of IEEE 802.15.4. Tested numerical values are analyzed using MATLAB and SPSS by defining the wireless link evaluation sensitivity. Result curves of RSSI to packet reception rate (PRR) and LQI to PRR we finally derive are shown that statistical means of RSSI and LQI can obtain the status information of receiving links more accurately, compared with the traditional wireless link evaluation using single RSSI and single LQI.

Combining Kalman Filtering with ZigBee Protocol to Improve Localization in Wireless Sensor Network

We propose a low-cost and low-power-consumption localization scheme for ZigBee-based wireless sensor networks (WSNs). Our design is based on the link quality indicator (LQI)—a standard feature of the ZigBee protocol—for ranging and the ratiometric vector iteration (RVI)—a light-weight distributed algorithm—modified to work with LQI measurements. To improve performance and quality of this system, we propose three main ideas: a cooperative approach, a coefficient delta () to regulate the speed of convergence of the algorithm, and finally the filtering process with the extended Kalman filter. The results of experiment simulations show acceptable localization performance and illustrate the accuracy of this method.