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Enabling multimedia aware vertical handover Management in Internet of Things based heterogeneous wireless networks

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Abstract

Enabling seamless connectivity in Internet of Things (IoT) based heterogeneous wireless networks and pervasive use of smartphones in daily life require high data speed and always-best-connected services. However, providing vertical handover management in heterogeneous wireless networks is a difficult and challenging task. Moreover, various issues are present in the current vertical handover management schemes such as inappropriate handover triggering, high handover delay, wrong network selection, etc. In order to address the aforementioned issues, we propose a generic vertical handover management scheme. Our research is twofold; firstly, the Mobile Node (MN) dynamically checks the data rate required by the applications running on the MN’s device. If the data rate drops below a predefined threshold, the MN initiates the handover. Secondly, the network selection is performed by considering various parameters such as end-to-end delay, jitter, Bit Error Rate, and packet loss. The Artificial Bee Colony (ABC) optimization algorithm uses the above parameters to select the target network with minimum handover delay and time. The proposed scheme is compared with the Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Fuzzy TOPSIS in context of energy consumption, throughput, average MN’s stay time in a network, handover delay, and handover time. The experimental results show that the proposed vertical handover management scheme outperforms the existing schemes used for similar purpose.

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Acknowledgements

This work was supported by the NRF Grant funded by the Korean Government (2015R1D1A1A01058171)

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Correspondence to Awais Ahmad or Gwanggil Jeon.

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Khan, M., Din, S., Gohar, M. et al. Enabling multimedia aware vertical handover Management in Internet of Things based heterogeneous wireless networks. Multimed Tools Appl 76, 25919–25941 (2017). https://doi.org/10.1007/s11042-017-4736-4

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  • DOI: https://doi.org/10.1007/s11042-017-4736-4

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