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New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

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Abstract

Multi-attribute decision-making (MADM) approaches are commonly used to model and solve the network interface selection problem in heterogeneous wireless networks. Despite their importance and advantages to deal with this issue, they suffer from the rank reversal problem (RRP) and the selection of the highest-ranking score network without considering the user’s/service requirements. In this paper, a novel method is introduced to solve the MADM limitations. Besides, the weights assignment technique is modelled as a multi-objective problem. Then, an extended version of the Whale Optimization Algorithm is applied to obtain the suitable weights of the decision criteria. The obtained results showed that applying the developed technique with MADM approaches reduces (sometimes avoids completely) the RRP by an average up to 94%, compared to Analytical Hierarchy Process. It also allows meeting the user’s/service requirements by optimizing data rate and packet loss, for streaming services, by an average up to 14.3 (kbps) and \(20\times 10^\text {6}\hbox {(ms)}\), respectively.

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References

  1. Wang L, Kuo GSG (2012) Mathematical modeling for network selection in heterogeneous wireless networks–a tutorial. IEEE Commun Surv Tutorials 15(1):271–292

    Article  Google Scholar 

  2. Gustafsson E, Jonsson A (2003) Always best connected. IEEE Wirel Commun 10(1):49–55

    Article  Google Scholar 

  3. Kassar M, Kervella B, Pujolle G (2008) An overview of vertical handover decision strategies in heterogeneous wireless networks. Comput Commun 31(10):2607–2620

    Article  Google Scholar 

  4. Trestian R, Ormond O, Muntean GM (2012) Game theory-based network selection: Solutions and challenges. IEEE Commun Surv Tutorials 14(4):1212–1231

    Article  Google Scholar 

  5. Senouci MA, Souihi S, Hoceini S, Mellouk A (2016) Qoe-based network interface selection for heterogeneous wireless networks: a survey and e-health case proposal. In: 2016 IEEE Wireless Communications and Networking Conference. IEEE; pp 1–6

  6. Xiao K, Li C (2018) Vertical handoff decision algorithm for heterogeneous wireless networks based on entropy and improved TOPSIS. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE; pp 706–710

  7. Zhong Y, Wang H, Lv H (2020) A cognitive wireless networks access selection algorithm based on MADM. Ad Hoc Netw 109:102286

    Article  Google Scholar 

  8. Chattate I, Khaili M, Bakkoury J (2019) A new fuzzy-TOPSIS based algorithm for enhancing decision making in a heterogeneous network. J Commun. 14(3):194–201

    Article  Google Scholar 

  9. Mansouri M, Leghris C (2020) A use of fuzzy TOPSIS to improve the network selection in wireless multiaccess environments. J Comput Netw Commun

  10. Evangeline CS, Kumaravelu VB (2020) AHP-FVIKOR based access network selection in vehicular communications. In: Inventive Communication and Computational Technologies. Springer; pp 829–837

  11. Singh P, Agrawal R (2019) AHP based network selection scheme for heterogeneous network in different traffic scenarios. Int J Inf Technol. pp 1–9

  12. Zhou L, Jiang SM, Yan T (2021) A network selection scheme based on the analytic hierarchy process for marine internet. Wirel Commun Mobile Comput (2021)

  13. Zhao S, Wang F, Ning Y, Xiao Y, Zhang D (2020) Vertical handoff decision algorithm combined improved entropy weighting with GRA for heterogeneous wireless networks. KSII Trans Internet Inf Syst 14(11)

  14. Yu H, Guo M, Yu J (2020) A novel heterogeneous wireless network selection algorithm based on INFAHP and IGRA. J Interconnect Netw 20(03):2050010

    Article  Google Scholar 

  15. Mefgouda B, Idoudi H (2021) COCOSO-based network interface selection algorithm for heterogeneous wireless networks. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE; pp 1–5

  16. Mansouri M, Leghris C (2019) A comparison between MADM methods and utility functions in the network selection context. In: International Conference on Mobile, Secure, and Programmable Networking. Springer; pp 201–210

  17. Khan S, Ahmad MI, Hussain F (2018) Exponential utility function based criteria for network selection in heterogeneous wireless networks. Electron Lett 54(8):529–531

    Article  Google Scholar 

  18. Tan X, Chen G, Sun H (2020) Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network. EURASIP J Wirel Commun Netw 2020(1):1–21

    Article  Google Scholar 

  19. Moreira PR, de Castro MC, de Boni MHL, Cardoso FF (2015) An approach for network selection based on artificial neural networks in heterogeneous wireless environments. ICWMC 2015:40

    Google Scholar 

  20. Chen L, Li H (2016) An MDP-based vertical handoff decision algorithm for heterogeneous wireless networks. In: 2016 IEEE Wireless Communications and Networking Conference. IEEE. pp 1–6

  21. Xie J, Gao W, Li C (2020) Heterogeneous network selection optimization algorithm based on a Markov decision model. China Commun 17(2):40–53

    Article  Google Scholar 

  22. Abbas N, Saade JJ (2015) A fuzzy logic based approach for network selection in WLAN/3G heterogeneous network. In: 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC). IEEE. pp 631–636

  23. Wang X, Liu B, Su X, Xu X, Xiao L (2019) Evolutionary game based heterogeneous wireless network selection with multiple traffics in 5G. In: 2019 26th International Conference on Telecommunications (ICT). IEEE. pp 80–84

  24. Adiwal M, Singh NP (2019) RAT selection for a low battery mobile device for future 5G networks. Int J Commun Syst 32(13):e4055

    Article  Google Scholar 

  25. Obayiuwana E, Falowo OE (2017) Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review. Wireless Netw 23(8):2617–2649

    Article  Google Scholar 

  26. Bendaoud F, Abdennebi M, Didi F (2018) Network selection in wireless heterogeneous networks: a survey. J Telecommun Inf Technol

  27. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  28. Drissi M, Oumsis M, Aboutajdine D (2017) A multi-criteria decision framework for network selection over LTE and WLAN. Eng Appl Artif Intell 66:113–127

    Article  Google Scholar 

  29. Saaty TL, Vargas LG (2013) The analytic network process. In: Decision Making with the Analytic Network Process. Springer. pp 1–40

  30. Ayağ Z, Özdemir RG (2009) A hybrid approach to concept selection through fuzzy analytic network process. Comput Ind Eng 56(1):368–379

    Article  Google Scholar 

  31. Barzilai J, Golany B (1994) AHP rank reversal, normalization and aggregation rules. INFOR Inf Syst Oper Res 32(2):57–64

    MATH  Google Scholar 

  32. Jahan A, Edwards KL (2015) A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Mater Des (1980-2015) 65:335–342

    Article  Google Scholar 

  33. de Farias Aires RF, Ferreira L (2019) A new approach to avoid rank reversal cases in the TOPSIS method. Comput Ind Eng 132:84–97

    Article  Google Scholar 

  34. Senouci MA, Mushtaq MS, Hoceini S, Mellouk A (2016) TOPSIS-based dynamic approach for mobile network interface selection. Comput Netw 107:304–314

    Article  Google Scholar 

  35. Chandavarkar BR, Guddeti RMR (2016) Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks. Comput Commun 83:81–97

    Article  Google Scholar 

  36. Senouci MA, Hoceini S, Mellouk A (2016) Utility function-based TOPSIS for network interface selection in heterogeneous wireless networks. In: 2016 IEEE International Conference on Communications (ICC). IEEE. pp 1–6

  37. Baghla S, Bansal S (2018) VIKOR MADM based optimization method for vertical handover in heterogeneous networks. Adv Syst Sci Appl 18(3):90–110

    Google Scholar 

  38. Alhabo M, Zhang L, Nawaz N (2019) GRA-based handover for dense small cells heterogeneous networks. IET Commun 13(13):1928–1935

    Article  Google Scholar 

  39. Yu HW, Zhang B (2019) A hybrid MADM algorithm based on attribute weight and utility value for heterogeneous network selection. J Netw Syst Manage 27(3):756–783

    Article  Google Scholar 

  40. Mansouri M, Leghris C (2019) New Manhattan distance-based fuzzy MADM method for the network selection. IET Commun 13(13):1980–1987

    Article  Google Scholar 

  41. Mefgouda B, Idoudi H (2022) A novel MADM based network interface selection approach with rank reversal avoidance in HWNs. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE. pp 2082–2087

  42. Lu Y, Chen Y, Huang S, Zhang M (2021) An access network selection algorithm for terrestrial-satellite networks based on a QoS guarantee. Math Probl Eng (2021)

  43. Almutairi AF, Hamed M, Landolsi MA, Algharabally M (2018) A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks. Telecommun Syst 68(2):151–161

    Article  Google Scholar 

  44. Almutairi AF, Al-Gharabally M, Salman AA (2021) Particle swarm optimization application for multiple attribute decision making in vertical handover in heterogenous wireless networks. J Eng Res 9(1)

  45. Yu HW, Zhang B (2018) A heterogeneous network selection algorithm based on network attribute and user preference. Ad Hoc Netw 72:68–80

    Article  Google Scholar 

  46. Goyal RK, Kaushal S, Sangaiah AK (2018) The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks. Appl Soft Comput 67:800–811

    Article  Google Scholar 

  47. Priya B, Malhotra J (2020) 5GAuNetS: an autonomous 5G network selection framework for Industry 4.0. Soft Comput 24(13):9507–9523

    Article  Google Scholar 

  48. Alhabo M, Zhang L (2018) Multi-criteria handover using modified weighted TOPSIS methods for heterogeneous networks. IEEE Access. 6:40547–40558

    Article  Google Scholar 

  49. Yu H, Ma Y, Yu J (2019) Network selection algorithm for multiservice multimode terminals in heterogeneous wireless networks. IEEE Access. 7:46240–46260

    Article  Google Scholar 

  50. Yu H, Chen L, Yu J (2020) Network selection algorithm based on chi-square distance in heterogeneous wireless networks. Wireless Pers Commun 111(3):1625–1643

    Article  Google Scholar 

  51. Guo X, Omar M, Mohd Zaini K, et al. (2020) Multiattribute access selection algorithm supporting service characteristics and user preferences in heterogeneous wireless networks. Wirel Commun Mobile Comput

  52. Radouche S, Leghris C (2020) Network selection based on cosine similarity and combination of subjective and objective weighting. In: 2020 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE; p. 1–7

  53. Radouche S, Leghris C (2021) New network selection algorithm based on cosine similarity distance and PSO in heterogeneous wireless networks. J Comput Netw Commun (2021)

  54. Behzadian M, Otaghsara SK, Yazdani M, Ignatius J (2012) A state-of the-art survey of TOPSIS applications. Expert Syst Appl 39(17):13051–13069

    Article  Google Scholar 

  55. Savitha K, Chandrasekar C (2011) Vertical handover decision schemes using SAW and WPM for network selection in heterogeneous wireless networks. arXiv preprint arXiv:1109.4490

  56. Got A, Moussaoui A, Zouache D (2020) A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert Syst Appl 141:112972

    Article  Google Scholar 

  57. Pollini GP (1996) Trends in handover design. IEEE Commun Mag 34(3):82–90

    Article  Google Scholar 

  58. Pahlavan K, Krishnamurthy P, Hatami A, Ylianttila M, Makela JP, Pichna R et al (2000) Handoff in hybrid mobile data networks. IEEE Pers Commun 7(2):34–47

    Article  Google Scholar 

  59. 3rd Generation Partnership Project (2003) Quality of Service (QoS) Concept and Architecture. 3GPP TS 23107

  60. Habbal A, Goudar SI, Hassan S (2017) Context-aware radio access technology selection in 5G ultra dense networks. IEEE Access 5:6636–6648

    Article  Google Scholar 

  61. Mirjalili S, Saremi S, Mirjalili SM, Coelho LS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  62. Raquel CR, Naval Jr PC (2005) An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. pp 257–264

  63. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  64. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

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  1. National School of Computer Science, University of Manouba, Manouba, Tunisia

    Brahim Mefgouda & Hanen Idoudi

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  1. Brahim Mefgouda
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Mefgouda, B., Idoudi, H. New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks. J Supercomput 79, 3580–3615 (2023). https://doi.org/10.1007/s11227-022-04791-y

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