基于隐朴素贝叶斯分类方法的垂直切换算法

Adaptive Vertical Switching Algorithm Based on Hidden Naive Bayesian Classification

为解决车辆在相对高速运动下产生网络间切换的"乒乓效应",根据隐朴素贝叶斯分类思想,突破原有贝叶斯决策中关于属性之间完全独立的假设,建立属性间的关系,同时引入自适应修正概率,降低切换次数,避免了运算的复杂度。仿真结果表明,改进算法与原算法及其他算法相比较,可以有效降低切换次数,并且拥有更低的运行时间,提升了在车联网环境下垂直切换的稳定性与效率。

In order to solve the'ping-pong effect'of network switching caused by vehicles moving at relatively high speed,according to the idea of Hidden Naive Bayesian Classification,the relationship between attributes is established by breaking through the assumption that attributes are completely independent in the original Bayesian decision-making.And self-adaptive correction probability is introduced to reduce the number of switching and avoid calculation complexity.The simulation results show that,compared with the original algorithm and other algorithms,the improved algorithm can effectively reduce the number of handoffs,and has lower running time,which improves the stability and efficiency of vertical handoff in the environment of vehicular networking.