基于时间序列分析的链路质量预测和稳定路由算法研究
doi: 10.3724/SP.J.1146.2010.00836
A Time Series Analysis-based Link Quality Prediction Algorithm and Its Application to Reliable Routing
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摘要: 针对现有链路质量预测算法不适用于实际通信场景的问题,该文提出一个不依赖于任何特定信号传输模型和节点移动模型、低复杂度的实时链路质量预测算法,并将其应用于稳定路由协议设计。通信节点存储并更新其邻节点的接收信号强度集合以构成时间序列,将时间序列分析中的局部线性核平滑方法和滑动窗口局部多项式预测方法引入链路质量判断及预测。在此基础上,结合跨层协作思想,提出了节点移动自适应的提前路由修复机制。仿真结果表明,该预测算法具有较高的预测准确度,并能显著增强路由稳定性,提高网络性能。Abstract: It has been proved that many popular link quality prediction algorithms perform much more poorly when they are applied to real environments. To address this problem, a low-complexity, on-line link quality prediction mechanism is constructed which can be applied to reliable routing protocol design. The algorithm does not require the use of any deterministic propagation model or mobility model; it relies simply on Received Signal Strength (RSS) to estimate link quality. The nodes monitor and store the links signal strength values to their communicating neighbors in order to obtain a time series of RSS measurements. Local linear kernel smoothing method and moving window local polynomial prediction method in time series analysis are introduced to construct an efficient link quality prediction mechanism, which provides significant cross-layer information for the design of mobility-adaptive proactive route repair scheme. Simulation results indicate that the proposed algorithm can provide high accuracy of link quality prediction, resulting in improved route stability and network performance.
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