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Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network

Overview of attention for article published in Sensors, July 2017
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Title
Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network
Published in
Sensors, July 2017
DOI 10.3390/s17071626
Pubmed ID
Authors

Radhwan Mohamed Abdullah, Zuriati Ahmad Zukarnain

Abstract

Transferring a huge amount of data between different network locations over the network links depends on the network's traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Student > Doctoral Student 4 11%
Student > Master 4 11%
Student > Bachelor 4 11%
Student > Postgraduate 3 9%
Other 6 17%
Unknown 8 23%
Readers by discipline Count As %
Computer Science 12 34%
Engineering 12 34%
Medicine and Dentistry 1 3%
Energy 1 3%
Unknown 9 26%