传感器网络中基于置信区间的数据聚合策略

为了减少能耗对于无线传感器网络生存时间的影响．提出了一种置信区间和禁忌算法相结合的数据聚合算法(CBTA)．CBTA根据簇内节点所采数据呈高斯分布的特点,使用抽样样本设定置信区间与总量模型的对比,有效地减少了数据发送量;同时为避免出现采集热点,采用禁忌算法对抽样数据进行禁忌水平线限制．与相关研究相比,CBTA采用全新的数据发送方式,能够有效降低能耗．仿真实验证明,CBTA能够很好地满足不同用户准确率的要求,且在网络寿命上大幅度优于同类算法．

A Data Aggregation Algorithm Based on Confidence Interval in Wireless Sensor Networks

For reducing energy consumption is essential for the life of wireless sensor networks, a confidence interval banding taboo-level of data aggregation protocol for long-lived sensor network is proposed. Considering that the intra-cluster data taking on Gaussian distribution characteristics, confidence interval banding taboo-level of data aggregation (CBTA) makes data sampling confidence interval compared with the total model, that can be effective in reducing the amount of data being sent. In order to avoid hot collected in cluster, taboo algorithm is used for sampling data to calculate the taboo-level. Compared with the previous related research, CBTA adopts new data sending model using spatial correlation of data, that will handle the energy efficiently better. Experiments show that CBTA can satisfy the desired accuracy given by system and the life of network is proved significantly lengthened.