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.

中文翻译：

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基于LQI和PRR的工业无线网络混合可靠路由算法

在工业无线网络（IWN）中，机器对机器（M2M）的通信经常受到工业环境中噪声的影响，导致通信可靠性的下降。在本文中，我们研究了如何在工业环境中通过 M2M 提高路由稳定​​性。我们首先比较不同的链路质量估计，例如信噪比 (SNR)、接收信号强度指标 (RSSI)、链路质量指标 (LQI)、数据包接收比 (PRR) 和预期传输计数 (ETX)。然后我们提出了一种结合 LQI 和 PRR 的链路质量估计。最后，我们为 IWN 提出了一种基于混合链路质量估计的可靠路由 (HLQEBRR) 算法，目的是最大化链路稳定性。此外，HLQEBRR 提供了检测节点故障的恢复机制，这提高了节点恢复的速度和准确性。基于 OMNeT++ 的仿真结果表明，我们的 HLQEBRR 算法在端到端传输延迟和丢包率方面明显优于集合树协议 (CTP) 算法，并且 HLQEBRR 算法以较小的额外成本实现了更高的可靠性。