Self-adaption link-quality detection algorithm for data collecting in OSN
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摘要: 提出了一种自适应链路侦测的数据收集算法,以提高机会传感网链路的预测精度与传输成功率:采用自适应链路侦测算法测量出各节点的实时网内链路质量权重因子,结合节点能量消耗模型,通过基于无味卡尔曼滤波的概率预测方法对节点间的链路质量进行量化计算,实现节点对通信链路的实时自适应预测与最优化选择,从而完成数据收集.仿真实验结果表明:新算法提高了最优化路径决策的预测精度,可以有效地增加消息的平均通信成功率,降低消息的平均传输延时.Abstract: In order to implement the data collecting in opportunistic sensor network (OSN), a self-adaption link-quality detection algorithm (SLDA) was proposed to enhance the prediction accuracy of the link and the data transmission rate. The new scheme adopted self-adaptive link-quality detection strategy to measure the real-time link quality weight factor, then it was combined with energy consumption model of mobile nodes to quantitatively predict optimal transmission link for message forwarding by means of the unscented Kalman filter (UKF). Simulation results show that SLDA increases the average delivery ratio and reduces the average delay in OSN, and performs well in the situation of sparse deployment of mobile nodes.
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