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. 2023 Nov 15;18(11):e0294411.
doi: 10.1371/journal.pone.0294411. eCollection 2023.

An adaptive optimized handover decision model for heterogeneous networks

Nada Ahmed Ezz-Eldien  1 , Heba M Abdel-Atty  1 , Mahmoud I Abdalla  2   3 , Korany R Mahmoud  4   5 , Mohamed F Abdelkader  1
Affiliations

An adaptive optimized handover decision model for heterogeneous networks

Nada Ahmed Ezz-Eldien et al. PLoS One. .

Abstract

A heterogeneous network (HetNet), combining different technologies, is considered a promising solution adopted by several upcoming generations of mobile networks to keep up with the rapid development of mobile users' requirements while improving network performance. In this scenario, a vertical handover (VHO) algorithm is responsible for ensuring the continuity of the ongoing user connection while moving within the coverage of the HetNet. Although various VHO algorithms were proposed, achieving efficient performance from both network and user perspectives remains challenging. This paper proposes an adaptive optimized vertical handover algorithm based on a multi-attribute decision-making (MADM) algorithm integrated with particle swarm optimization and gravitational search algorithm (PSOGSA) as a framework to implement the handover process. The algorithm includes three main ideas. Firstly, a network selection framework is proposed considering the most important criteria, including signal strength and other networks' attributes, along with users' characteristics regarding their mobility and service preferences. Secondly, two new parameters are introduced as control handover parameters named load factor (LF) and score priority (SP) to reduce unnecessary handovers and the overall HetNet power consumption while achieving balanced load distribution. Lastly, the desired aims are formulated as an objective function, then the PSOGSA algorithm is used to reach the optimal values of both LF and SP, which will be considered when executing the handover algorithm. The presented algorithm is simulated in a heterogeneous wireless network where the fifth-generation (5G) wireless technology coexists with other radio access networks to improve the evaluation field of the proposed algorithm. Also, the proposed algorithm's performance is evaluated in the case of using various MADM algorithms. The simulation results show that the proposed adaptive optimized approach attains efficient performance by decreasing unnecessary handovers by more than 40% and achieving much better load distribution by around 20% to 40%, outperforming traditional handover approaches.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. System model.
Fig 2
Fig 2. Flowchart of weighting using the AHP method.
Fig 3
Fig 3. Flowchart of weighting using the entropy method.
Fig 4
Fig 4. MADM methods’ general flowchart.
Fig 5
Fig 5. Flow chart of the PSOGSA scenario.
Fig 6
Fig 6. The flow chart of the proposed PSOGSAHO scenario.
Fig 7
Fig 7. Weights for voice application.
Fig 8
Fig 8. Weights for video application.
Fig 9
Fig 9. Weights for data application.
Fig 10
Fig 10. Candidate networks selection percentages.
Fig 11
Fig 11. Handover percentage results.
Fig 12
Fig 12. Ping-Pong percentage results.
Fig 13
Fig 13. Load distribution results.
Fig 14
Fig 14. Power consumption results.
Fig 15
Fig 15. Handover percentage results.
Fig 16
Fig 16. Ping-Pong percentage results.
Fig 17
Fig 17. Load distribution results.
Fig 18
Fig 18. Power consumption results.
Fig 19
Fig 19. Handover percentage versus number of time samples.
Fig 20
Fig 20. Ping-Pong percentage versus number of time samples.
Fig 21
Fig 21. Load distribution versus number of time samples.
Fig 22
Fig 22. Power consumption versus number of time samples.
Fig 23
Fig 23. Handover percentage versus number of users.
Fig 24
Fig 24. Ping-Pong percentage versus number of users.
Fig 25
Fig 25. Load distribution versus number of users.
Fig 26
Fig 26. Power consumption versus number of users.
See this image and copyright information in PMC

References

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