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On-line learning and prediction of link quality in wireless sensor networks

机译：无线传感器网络中链路质量的在线学习和预测

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Communication between sensor nodes in a Wireless Sensor Network (WSN) faces energy and bandwidth constraints. The dynamic behavior over time of the wireless channels makes ephemeral the neighborhood relation between sensors. Link quality estimation is critical for many WSN applications because it drastically influences the success of transmissions. In this paper we resort to machine learning methods to predict the short term evolution of link quality, in order to switch the data transmission on a better quality link. The problem is modeled as a game of prediction based on experts advice, using the Link-Quality Indicator (LQI) metric. A decision maker, called forecaster, advised by several experts, predicts the LQI values. The forecaster is able to learn how to adapt its strategy to predict values as close as possible to real LQI values. The proposed learning and prediction model presents a great flexibility: it is a general model that can be easily adapted to different link-quality metrics or prediction methods.
机译：无线传感器网络（WSN）中传感器节点之间的通信面临能量和带宽限制。无线信道随时间的动态行为使短暂的传感器之间的邻居关系。链路质量估计对于许多WSN应用而言至关重要，因为它会严重影响传输的成功。在本文中，我们采用机器学习方法来预测链路质量的短期发展，以便将数据传输切换到质量更高的链路上。使用链接质量指标（LQI）度量，将问题建模为基于专家建议的预测游戏。由几位专家建议的决策者称为预报器，可以预测LQI值。预测器能够学习如何调整其策略以预测尽可能接近实际LQI值的值。所提出的学习和预测模型具有很大的灵活性：这是一个通用模型，可以轻松地适应不同的链路质量指标或预测方法。

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