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30.04.2020 | Original Article | Ausgabe 12/2020

International Journal of Machine Learning and Cybernetics 12/2020

A novel handover detection model via frequent trajectory patterns mining

Zeitschrift:
International Journal of Machine Learning and Cybernetics > Ausgabe 12/2020
Autoren:
Nan Han, Shaojie Qiao, Guan Yuan, Rui Mao, Kun Yue, Chang-an Yuan
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Abstract

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.

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