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A novel handover detection model via frequent trajectory patterns mining

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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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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grant Nos. 61772091, 61802035, 61962006, 71701026; the Sichuan Science and Technology Program under Grant Nos. 2018JY0448, 2019YFG0106, 2019YFS0067; the Natural Science Foundation of Guangxi under Grant No. 2018GXNSFDA138005; Guangdong Province Key Laboratory of Popular High Performance Computers under Grant No. 2017B030314073; the Innovative Research Team Construction Plan in Universities of Sichuan Province under Grant No. 18TD0027; the Key R&D Program of Guangdong province under Grant No. 2018B030325002.

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Authors and Affiliations

  1. School of Management, Chengdu University of Information Technology, Chengdu, 610103, China

    Nan Han

  2. School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Shaojie Qiao

  3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Guan Yuan

  4. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, 518060, China

    Rui Mao

  5. Guangdong Province Engineering Center of China-made High Performance Data Computing System, Shenzhen, 518060, China

    Rui Mao

  6. School of Information Science and Engineering, Yunnan University, Kunming, 650500, China

    Kun Yue

  7. School of Computer and Information Engineering, Nanning Normal University, Nanning, 530299, China

    Chang-an Yuan

  8. Guangxi College of Education, Nanning, 530007, China

    Chang-an Yuan

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  1. Nan Han
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Correspondence to Shaojie Qiao.

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Han, N., Qiao, S., Yuan, G. et al. A novel handover detection model via frequent trajectory patterns mining. Int. J. Mach. Learn. & Cyber. 11, 2587–2606 (2020). https://doi.org/10.1007/s13042-020-01126-2

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