强噪声海量物联网数据处理中节点选择算法研究

当前节点选择算法在强噪声干扰下,通过优先级理论实现节点选择,存在安全性差、能耗高、整体性能低下的弊端,为此,提出一种新的强噪声海量物联网数据处理中节点选择算法。将数字序列中某点的值用与该点相邻的各点值的中值替换,通过中值滤波法对物联网中的强噪声进行去除,完成强噪声干扰去除后。通过基于后悔函数的平衡剩余能量节点选择算法对海量物联网数据处理中节点进行选择。依据误码率选择满足要求的节点作为候选节点,通过减少无效处理增强能量性能。利用后悔函数对节点相对物联网的剩余能量进行估算,依据节点相对于其他节点的剩余能量确定延迟时间,从而实现节点选择。实验结果表明,采用所提算法对海量物联网数据处理中节点进行选择,不仅安全性高,而且节能性好、整体性能高。

Strong noise mass iot data processing node selection algorithm in the study

The current node selection algorithm in strong noise background by priority theory to realize node selection, poor safety, high energy consumption, low overall performance drawbacks, therefore, node selection algorithm presents a strong noise mass new network data processing. The sequence number of a point in value and the median of each sample point adjacent to the replacement, removal of strong noise by median filtering method for the IOT, complete removal of strong noise, the regret function balance node residual energy node selection algorithm of mass data processing were selected based on network. According to the bit error rate, the nodes that meet the requirements are selected as candidate nodes, which can enhance the energy performance by reducing the invalid processing. By using regret function to estimate the residual energy of the relative Internet of things, the delay time is determined according to the remaining energy of the node relative to other nodes, so as to realize the node selection. The experimental results show that the proposed algorithm is used to select nodes in the data processing of massive Internet of things, which is not only safe, but also good at energy saving and high performance.