As the cellular wireless communication techniques grow rapidly, the cells become smaller than the traditional communication system, then the handover events are very frequent and need to support a large number of users, and handover detection has become a very active research direction in a mobile computing environment. In order to copy with the problem of frequent handover operations between base stations in current cellular communication networks as cybernetic systems, we propose a novel handover detection approach by integrating frequent trajectory patterns mining and location prediction techniques. The proposed model contains the following essential steps: (1) mining frequent trajectory patterns from large-scale historical trajectory databases by applying an improved Apriori-like rule-based machine learning algorithm, which finds the intersection of candidate items by applying the trajectory connection operation instead of calculating the support count of each trajectory patterns and the candidate items are considerably reduced; (2) discovering movement rules based on the frequent trajectory pattern set by finding the postfix items of given prefix items satisfying the minimum support threshold; (3) inferring the future locations of moving objects by applying the movement rules matching strategy; (4) determining whether or not to perform handover detection across base stations in a cellular network beyond the discovered prediction results, according to the coverage area of cellular networks. Extensive experiments were conducted on the mobile datasets and the experimental results demonstrate the advantages of the proposed algorithm from the following four aspects: (1) the accuracy of handover detection is above 95% at average which is a very satisfactory result in a mobile computing environment; (2) the time cost is less than 20 s when the number of movement rules and handover detection is 1000, which further shows the merit of the runtime performance of the proposed method; (3) the frequent-trajectory-patterns based handover detection algorithm can successfully avoid the ping-pong effect due to unnecessary handover operations; (4) and lastly significantly reduce the error rate of frequent handover decisions and the average unnecessary handover rate is lower than 0.05 when compared with the state-of-the-art methods.

随着蜂窝无线通信技术的飞速发展，其蜂窝比传统的通信系统要小，因此切换事件非常频繁，需要支持大量用户，并且切换检测已成为移动计算中非常活跃的研究方向环境。为了解决当前作为控制论系统的蜂窝通信网络中基站之间频繁切换操作的问题，我们提出了一种通过结合频繁轨迹模式挖掘和位置预测技术的新颖切换检测方法。提出的模型包含以下基本步骤：（1）通过使用改进的基于Apriori规则的机器学习算法从大规模历史轨迹数据库中挖掘频繁轨迹模式，通过应用轨迹连接操作而不是计算每个轨迹模式的支持次数来找到候选项的交集，从而大大减少了候选项；（2）通过找到给定的满足最小支持阈值的前缀项的后缀项，根据频繁轨迹模式设置运动规则；（3）通过应用运动规则匹配策略推断运动对象的未来位置；（4）根据蜂窝网络的覆盖范围，确定是否超出发现的预测结果跨蜂窝网络中的基站进行切换检测。在移动数据集上进行了广泛的实验，实验结果从以下四个方面证明了该算法的优势：（1）切换检测的平均准确率超过95％，这在移动计算环境中是非常令人满意的结果; （2）当移动规则和切换检测的数量为1000时，时间成本小于20 s，这进一步表明了该方法运行时性能的优点。（3）基于频繁轨迹模式的切换检测算法可以成功避免由于不必要的切换操作而产生的乒乓效应；（4）并且最后显着降低了频繁切换决策的错误率，并且与最新技术方法相比，平均不必要的切换率低于0.05。（1）切换检测的准确性平均超过95％，这在移动计算环境中是非常令人满意的结果；（2）当移动规则和切换检测的数量为1000时，时间成本小于20 s，这进一步表明了该方法运行时性能的优点。（3）基于频繁轨迹模式的切换检测算法可以成功避免由于不必要的切换操作而产生的乒乓效应；（4）并且最后显着降低了频繁切换决策的错误率，并且与最新技术方法相比，平均不必要的切换率低于0.05。（1）切换检测的准确性平均超过95％，这在移动计算环境中是非常令人满意的结果；（2）当移动规则和切换检测的数量为1000时，时间成本小于20 s，这进一步表明了该方法运行时性能的优点。（3）基于频繁轨迹模式的切换检测算法可以成功避免由于不必要的切换操作而产生的乒乓效应；（4）并且最后显着降低了频繁切换决策的错误率，并且与最新技术方法相比，平均不必要的切换率低于0.05。（2）当移动规则和切换检测的数量为1000时，时间成本小于20 s，这进一步表明了该方法运行时性能的优点。（3）基于频繁轨迹模式的切换检测算法可以成功避免由于不必要的切换操作而产生的乒乓效应；（4）并且最后显着降低了频繁切换决策的错误率，并且与最新技术方法相比，平均不必要的切换率低于0.05。（2）当移动规则和切换检测的数量为1000时，时间成本小于20 s，这进一步表明了该方法运行时性能的优点。（3）基于频繁轨迹模式的切换检测算法可以成功避免由于不必要的切换操作而产生的乒乓效应；（4）并且最后显着减少了频繁切换决策的错误率，并且与最新技术方法相比，平均不必要的切换率低于0.05。