Handover detection approach based on trajectory data mining techniques. Issue 16 (28th September 2018)

• Record Type: Journal Article
• Authors: Han, Nan; Qiao, Shaojie; Liu, Dunhu; Ding, Peng; Zhang, Yongqing; Xiong, Xi; Wang, Mengran; Gutierrez, Louis Alberto
• Is Part Of: Journal of engineering. Volume 2018:Issue 16(2018)
• Page Start: 1534
• Page End: 1537
• Publication Date: 2018-09-28
• Subjects: mobility management (mobile radio) -- cellular radio -- data mining; handover detection approach -- trajectory data mining techniques -- handover approaches -- cellular communication networks -- frequent trajectory patterns -- massive historical trajectories -- improved Apriori algorithm -- cellular network -- discovered prediction results -- frequent‐trajectory‐based handover detection -- unnecessary frequent handover operations; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8326
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : In order to overcome the disadvantages of existing handover approaches in cellular communication networks, a new handover detection approach based on trajectory data mining techniques is presented. It discovers frequent trajectory patterns from massive historical trajectories of moving objects by applying the improved Apriori algorithm and determines whether or not to perform handover in a cellular network based on the discovered prediction results, according to the coverage area of cellular networks. Experiments were conducted on synthetic datasets and the results show that: the proposed frequent‐trajectory‐based handover detection model can successfully avoid the ping‐pong effect caused by unnecessary frequent handover operations, greatly reduce the error rate of handover, and the accuracy of handover detection is very high.