JACIII Vol.23 No.6 pp. 1063-1072
doi: 10.20965/jaciii.2019.p1063


Enhanced Intersystem Handover Algorithm for Heterogeneous Wireless Networks

Topside E. Mathonsi*, Okuthe P. Kogeda**, and Thomas O. Olwal*

*Tshwane University of Technology
Staatsartillerie Road, Pretoria West, Pretoria 0001, South Africa

**Department of Computer Science & Informatics, University of the Free State
Bloemfontein 9300, South Africa

October 8, 2018
July 25, 2019
November 20, 2019
FAHP, GPT, IH algorithm, MADM, MOORA

Current and future wireless network architectures consist of several access technologies to support numerous traffic types and enable mobile devices to be connected anytime, anywhere. However, providing a rapid seamless connectivity and service continuity between such various access technologies remains a challenge. This is mainly because the previously proposed handover algorithms have failed to predict the future values of the measured received signal strength needed for rapid handover process. In addition, existing handover algorithms are not adaptable to the changes of the network conditions and user preferences. This leads to erroneous network selection, packet loss, and ping-pong effect due to high-ranking abnormality. In this study, an intersystem handover (IH) algorithm has been designed by integrating grey prediction theory, multiple-attribute decision making, fuzzy analytic hierarchy process, and multi-objective optimization ratio analysis. Network Simulator 2 has been applied to evaluate the performance of the proposed IH algorithm when compared to the fuzzy logic-based vertical handover (FLBVH) algorithm and the adaptive neuro-fuzzy inference system (ANFIS) algorithm. On average, the proposed IH algorithm has shown 1.1 s handover delay, 5% packet loss, 1.6% probability of ping-pong effect, and 97.8% better throughput performance than the ANFIS algorithm and FLBVH algorithm, respectively, for a 100-s time interval.

Cite this article as:
T. Mathonsi, O. Kogeda, and T. Olwal, “Enhanced Intersystem Handover Algorithm for Heterogeneous Wireless Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.6, pp. 1063-1072, 2019.
Data files:
  1. [1] N. Rajule and B. Ambudkar, “Seamless and Optimised Vertical Handover Algorithm,” Proc. of the 2015 Int. Conf. on Computing Communication Control and Automation, pp. 195-199, 2015.
  2. [2] T. E. Mathonsi, O. P. Kogeda, and T. O. Olwal, “Intersystem Handover Decision Model for Heterogeneous Wireless Networks,” Proc. of IEEE Open Innovations Conf., pp. 1-7, 2018.
  3. [3] T. E. Mathonsi, O. P. Kogeda, and T. O. Olwal, “A Survey of Intersystem Handover Algorithms in Heterogeneous Wireless Networks,” Asian J. of Information Technology, Vol.16, Issue 6, pp. 422-439, 2017.
  4. [4] T. E. Mathonsi and O. P. Kogeda, “Handoff Delay Reduction Model for Heterogeneous Wireless Networks,” Proc. of 2016 IST-Africa Week Conf., pp. 1-7, 2016.
  5. [5] T. E. Mathonsi, O. P. Kogeda, and T. O. Olwal, “Intersystem Handover Delay Minimization Model for Heterogeneous Wireless Networks,” Proc. of South African Institute of Computer Scientists and Information Technologists (SAICSIT 2017), pp. 377, 2017.
  6. [6] B. Omoniwa, R. Hussain, J. Ahmed, A. Iqbal, A. Murkaz, Q. Ul-Hasan, and S. A. Malik, “A novel model for minimizing unnecessary handover in heterogeneous networks,” Turkish J. of Electrical Engineering & Computer Sciences, Vol.26, No.4, pp. 1771-1782, 2018.
  7. [7] A. G. Mahira and M. S. Subhedar, “Handover Decision in Wireless Heterogeneous Networks Based on Feedforward Artificial Neural Network,” Proc. of the Int. Conf. on Computational Intelligence in Data Mining (CIDM), pp. 663-669, 2017.
  8. [8] S. I. Goudar, A. Habbal, and S. Hassan, “Context-Aware Multi-Criteria Framework for RAT Selection in 5G Networks,” Advanced Science Letters, Vol.23, No.6, pp. 5163-5167, 2017.
  9. [9] J. Orimolade and N. Ventura, “Intelligent Access Network Selection for Data Offloading in Heterogeneous Networks,” Proc. of the 12th IEEE 2015 AFRICON Int. Conf.: Green Innovation for African Renaissance, 5pp., 2015.
  10. [10] F. Azzali, O. Ghazali, and M. H. Omar, “Fuzzy Logic-based Intelligent Scheme for Enhancing QoS of Vertical Handover Decision in Vehicular Ad-hoc Networks,” Proc. of the Int. Research and Innovation Summit (IRIS 2017), IOP Conf. Series: Materials Science and Engineering, Vol.226, Article No.012081, 2017.
  11. [11] A. Ben Zineb, M. Ayadi, and S. Tabbane, “Cognitive Radio Networks Management using an ANFIS Approach with QoS/QoE Mapping Scheme,” Proc. of the 2015 Int. Symp. on Networks, Computers and Communications (ISNCC), 6pp., 2015.
  12. [12] L. Zhang, L. Ge, X. Su, and J. Zeng, “Fuzzy Logic based Vertical Handover Algorithm for Trunking System,” Proc. of the 2017 26th Wireless and Optical Communication Conf. (WOCC), 5pp., 2017.
  13. [13] A. Mehbodniya, F. Kaleem, K. K. Yen, and F. Adachi, “A Fuzzy MADM Ranking Approach for Vertical Mobility in Next Generation Hybrid Networks,” Proc. of the IV Int. Congress on Ultra Modern Telecommunications and Control Systems, pp. 262-267, 2012.
  14. [14] A. Ben Zineb, M. Ayadi, and S. Tabbane, “QoE-Fuzzy VHO Approach for Heterogeneous Wireless Networks (HWNs),” Proc. of the 2016 IEEE 30th Int. Conf. on Advanced Information Networking and Applications, pp. 949-956, 2016.
  15. [15] D. Zhang, Y. Zhang, N. Lv, and Y. He, “An access selection algorithm based on GRA integrated with FAHP and entropy weight in hybrid wireless environment,” Proc. of the 2013 7th Int. Conf. on Application of Information and Communication Technologies, 5pp., 2013.
  16. [16] K. Shanmugam, “A novel candidate network selection based handover management with fuzzy logic in heterogeneous wireless networks,” Proc. of the 2017 4th Int. Conf. on Advanced Computing and Communication Systems (ICACCS), 6pp., 2017.
  17. [17] E. Obayiuwana and O. Falowo, “A New Network Selection Algorithm for Group Calls over Heterogeneous Wireless Networks with Dynamic Multi-Criteria,” Proc. of the 13th IEEE Annual Consumer Communications & Networking Conf. (CCNC), pp. 491-494, 2016.
  18. [18] S. Maaloul, M. Afif, and S. Tabbane, “Handover Decision in Heterogeneous Networks,” Proc. of the 2016 IEEE 30th Int. Conf. on Advanced Information Networking and Applications (AINA), pp. 588-595, 2016.
  19. [19] D. Guo and X. Li, “An adaptive vertical handover algorithm based on the analytic hierarchy process for heterogeneous networks,” Proc. of the 2015 12th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2059-2064, 2015.
  20. [20] A. H. Jafari and H. S. Shahhoseini, “A location aware history-based approach for network selection in heterogeneous wireless networks,” Turkish J. Electrical Engineering & Computer Science, Vol.24, No.4, pp. 2929-2948, 2016.
  21. [21] D. Cheelu, M. R. Babu, and P. V. Krishna, “A Study of Vertical Handoff Decision Strategies in Heterogeneous Wireless Networks,” Int. J. of Engineering and Technology (IJET), Vol.5, No.3, pp. 2541-2554, 2013.
  22. [22] E. Obayiuwana and O. E. Falowo, “Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review,” Wireless Networks, Vol.23, Issue 8, pp. 2617-2649, 2017.
  23. [23] A. Ben Zineb, M. Ayadi, and S. Tabbane, “Fuzzy MADM based vertical handover algorithm for enhancing network performances,” Proc. of the 2015 23rd Int. Conf. on Software, Telecommunications and Computer Networks (SoftCOM), pp. 153-159, 2015.
  24. [24] Z. Quan, N. Di, H. Jue, and Y. Yanchen, “Multiple Attribute Decision Making in High-Tech Project Evaluation,” Proc. of the 2015 7th Int. Conf. on Measuring Technology and Mechatronics Automation, pp. 674-677, 2015.
  25. [25] E. Kayacan, B. Ulutas, and O. Kaynak, “Grey system theory-based models in time series prediction,” Expert Systems with Applications, Vol.37, Issue 2, pp. 1784-1789, 2010.
  26. [26] Q. Yu and Y. Shen, “Research of information security risk prediction based on grey theory and ANP,” Proc. of the 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conf. (IMCEC), pp. 107-113, 2016.
  27. [27] J. Zhang and Y. Lou, “Water level prediction based on improved grey RBF neural network model,” Proc. of the 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conf. (IMCEC), pp. 775-779, 2016.
  28. [28] L. Zhang and S. Gao, “A novel weights generating approach for multiple attribute decision making under interval-valued intuitionistic fuzzy environment,” Proc. of the 2017 29th Chinese Control and Decision Conf. (CCDC), pp. 4406-4409, 2017.
  29. [29] W. K. M. Brauers, E. K. Zavadskas, F. Peldschus, and Z. Turskis, “Multi-objective decision-making for road design,” Transport, Vol.23, Issue 3, pp. 183-193, 2010.
  30. [30] E. A. Adalı and A. T. Işık, “The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem,” J. of Industrial Engineering Int., Vol.13, Issue 2, pp. 229-237, 2017.
  31. [31] R. Kumar and A. Ray, “Selection of Material Under Conflicting Situation Using Simple Ratio Optimization Technique,” Proc. of 4th Int. Conf. on Soft Computing for Problem Solving, pp. 513-519, 2015.
  32. [32] A. Sarkar, S. C. Panja, D. Das, and B. Sarkar, “Developing an efficient decision support system for non-traditional machine selection: an application of MOORA and MOOSRA,” Production & Manufacturing Research, Vol.3, Issue 1, pp. 324-342, 2015.
  33. [33] S. A. Hussain and A. Saeed, “An Analysis of Simulators for Vehicular Ad Hoc Networks,” J. of World Applied Sciences, Vol.23, No.8, pp. 1044-1048, 2013.
  34. [34] L. Ndlovu, O. P. Kogeda, and M. Lall, “Enhanced Service Discovery Model for Wireless Mesh Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 44-53, 2018.

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