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In fifth generation heterogeneous network, small cell is developed to compensate the growing demand for mobile data services. Due to the smaller size of cell, users have a short duration of connection, however, the user may also have the need of handoff frequently. At the time of handoff, different networks are available with different data rate and different other parameters. So, there is the need of frequent selection for the optimal network. In this paper, a utility-aware optimization algorithm has been proposed for network selection in a heterogeneous environment of Wi-Fi, WiMAX, WLAN, LTE, UMTS, and GPRS network. The weight factor is proposed for modified Jaya algorithm which is calculated by the analytical hierarchical process, standard deviation, and entropy method. Different applications are considered such as video, voice, web browsing and email transfer in which available bandwidth, packet jitter, packet loss, cost per byte are taken as dominant attributes, respectively. According to the dominant factor, different networks are selected for different applications because the requirement of all applications cannot be fulfilled by one network. Finally, the proposed algorithm is compared with multi-attribute decision making algorithms and game theory and accuracy of the proposed algorithm is calculated. The accuracy of proposed algorithm is higher as compared to the other algorithms and at the same time, this algorithm requires less computation which can further reduce the handoff latency and failure probability. Hence, the performance of handoff can be improved by using modified Jaya algorithm.
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Mastrosimone, A., & Panno, D. (2017). Moving network based on mmWave technology: A promising solution for 5G vehicular users. Wireless Networks. https://doi.org/10.1007/s11276-017-1479-0.
Shuminoski, T., & Janevski, T. (2016). 5G mobile terminals with advanced QoS-based user-centric aggregation (AQUA) for heterogeneous wireless and mobile networks. Wireless Networks,22(5), 1553–1570. CrossRef
Chinnadurai, S., et al. (2017). User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications. AEU-International Journal of Electronics and Communications,78, 181–191. CrossRef
Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications,35(9), 1936–1947. CrossRef
Zhang, H., Jiang, C., Cheng, J., & Leung, V. C. M. (2015). Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks. IEEE Wireless Communications,22(3), 92–99. CrossRef
Zhang, H., Jiang, C., Mao, X., & Chen, H. H. (2016). Interference-limited resource optimization in cognitive femtocells with fairness and imperfect spectrum sensing. IEEE Transactions on Vehicular Technology,65(3), 1761–1771. CrossRef
Zhang, H., Wang, B., Long, K., Cheng, J., & Leung, V. C. M. (2017). Energy-efficient resource allocation in heterogeneous small cell networks with wifi spectrum sharing. In Proceedings of IEEE Globecom.
Zhang, H., Liu, H., Cheng, J., & Leung, V. C. M. (2017). Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Transactions on Communications,6778(c), 1–12.
Hu, S., Wang, X., & Shakir, M. Z. (2015). A MIH and SDN-based framework for network selection in 5G HetNet: Backhaul requirement perspectives. In IEEE international conference on communication workshop ICCW 2015 (pp. 37–43).
Wu, Y., Hu, F., Zhu, Y., Kumar, S., & Member, S. (2017). Optimal spectrum handoff control for CRN based on hybrid priority queuing and multi-teacher apprentice learning. IEEE Transactions on Vehicular Technology,66(3), 2630–2642. CrossRef
Charilas, D. E., & Panagopoulous, A. D. (2010). Multiaccess radio network enviroments. IEEE Vehicular Technology Magazine,5(4), 40–49. CrossRef
Chonggang, W., Sohraby, K., Jana, R., Lusheng, J., & Daneshmand, M. (2009). Network selection in cognitive radio systems. In Global telecommunications conference, GLOBECOM 2009. IEEE (pp. 1–6).
Sheikholeslami, F., Nasiri-kenari, M., & Ashtiani, F. (2015). Optimal probabilistic initial and target channel selection for spectrum handoff in cognitive radio networks. IEEE Transactions on Wireless Communications,14(1), 570–584. CrossRef
Kumar, A., Mallik, R. K., & Schober, R. (2014). A probabilistic approach to modeling users’ network selection in the presence of heterogeneous wireless networks. IEEE Transactions on Vehicular Technology,63(7), 3331–3341. CrossRef
El Helou, M., Ibrahim, M., Lahoud, S., Khawam, K., Mezher, D., & Cousin, B. (2015). A network-assisted approach for rat selection in heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications,33(6), 1055–1067. CrossRef
He, H., Li, X., Feng, Z., Hao, J., Wang, X., & Zhang, H. (2017). An adaptive handover trigger strategy for 5G C/U plane split heterogeneous network. In 2017 IEEE 14th international conference on mobile ad hoc and sensor systems (pp. 476–480).
Kumar, K., Prakash, A., & Tripathi, R. (2017). Spectrum handoff scheme with multiple attributes decision making for optimal network selection in cognitive radio networks. Digital Communications and Networks,3(3), 164–175. CrossRef
Verma, R., & Singh, N. P. (2013). GRA based network selection in heterogeneous wireless networks. Wireless Personal Communications,72(2), 1437–1452. CrossRef
Martinez-Morales, J. D., Pineda-Rico, U., & Stevens-Navarro, E. (2010). Performance comparison between MADM algorithms for vertical handoff in 4G networks. In IEEE computing science and automatic control (CCE), 2010 7th international conference on electrical engineering (pp. 309–314).
Zhang, H., Jiang, C., Cheng, J., Peng, M., & Leung, V. C. M. (2017). Editorial: Game theory for 5G wireless networks. Mobile Networks and Applications,22(3), 526–528. CrossRef
Trestian, R., Ormond, O., & Muntean, G. (2012). Game theory-based network selection: Solutions and challenges. IEEE Communications Surveys & Tutorials,14(4), 1212–1231. CrossRef
Trestian, R., Ormond, O., & Muntean, G.-M. (2011). Reputation-based network selection mechanism using game theory. Physical Communication,4(3), 156–171. CrossRef
Niyato, D., & Hossain, E. (2009). Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach. IEEE Transactions on Vehicular Technology,58(4), 2008–2017. CrossRef
Liu, B., Tian, H., Wang, B., & Fan, B. (2014). AHP and game theory based approach for network selection in heterogeneous wireless networks. In Consumer communications and networking conference (pp. 973–978).
Vassaki, S., Panagopoulos, A. D., & Constantinou, P. (2009). Bandwidth allocation in wireless access networks: Bankruptcy game vs cooperative game. In International conference on ultra modern telecommunications & workshops (pp. 1–4).
Xu, K., Wang, K.-C., Amin, R., Martin, J., & Izard, R. (2015). A fast cloud-based network selection scheme using coalition formation games in vehicular networks. IEEE Transactions on Vehicular Technology,64(11), 5327–5339. CrossRef
Niyato, D., & Hossain, E. (2006). A cooperative game framework for bandwidth allocation in 4G heterogeneous wireless networks. In IEEE international conference on communications (pp. 4357–4362).
Trestian, R., Ormond, O., & Muntean, G. M. (2014). Enhanced power-friendly access network selection strategy for multimedia delivery over heterogeneous wireless networks. IEEE Transactions on Broadcasting,60(1), 85–101. CrossRef
Nguyen-Vuong, Q.-T., Agoulmine, N., Cherkaoui, E. H., & Toni, L. (2013). Multicriteria optimization of access selection to improve the quality of experience in heterogeneous wireless access networks. IEEE Transactions on Vehicular Technology,62(4), 1785–1800. CrossRef
Nguyen-Vuong, Q.-T., Ghamri-Doudane, Y., & Agoulmine, N. (2008). On utility models for access network selection in wireless heterogeneous networks. In Network operations and management symposium (pp. 144–151).
Monteiro, V. F., Sousa, D. A., Maciel, T. F., Lima, F. R. M., Rodrigues, E. B., & Cavalcanti, F. R. P. (2015). Radio resource allocation framework for quality of experience optimization in wireless networks. IEEE Network,29(6), 33–39. CrossRef
Alkhawlani, M., & Ayesh, A. (2008). Access network selection based on fuzzy logic and genetic algorithms. Advances in Artificial Intelligence,2008, 1–12. CrossRef
Hardiansyah, H. (2013). A modified particle swarm optimization technique for economic load dispatch with valve-point effect. International Journal of Intelligent Systems and Applications,5(7), 32–41. CrossRef
Yue, Y., Li, J., Fan, H., & Qin, Q. (2016). Optimization-based artificial bee colony algorithm for data collection in large-scale mobile wireless sensor networks. Journal of Sensors,1, 2016.
Venkata, R. (2016). Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems”. International Journal of Industrial Engineering Computations,7, 19–34. CrossRef
Chang, C.-J., Tsai, T.-L., & Chen, Y.-H. (2009). Utility and game-theory based network selection scheme in heterogeneous wireless networks. In IEEE wireless communications and networking conference (pp. 1–5).
Bacci, G., Lasaulce, S., Saad, W., & Sanguinetti, L. (2016). Game theory for networks: A tutorial on game-theoretic tools for emerging signal processing applications. IEEE Signal Processing Magazine,33(1), 94–119. CrossRef
Shuo, Z., & Qi, Z. H. U. (2014). Heterogeneous wireless network selection algorithm based on group decision. The Journal of China Universities of Posts and Telecommunications,21(3), 1–9. CrossRef
Delgado, A., & Romero, I. (2016). Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru. Environmental Modelling and Software,77, 108–121. CrossRef
Rao, R. V., & Rai, D. P. (2017). Optimisation of welding processes using quasi-oppositional-based Jaya algorithm. Journal of Experimental & Theoretical Artificial Intelligence,29(5), 1–19. CrossRef
Rao, R. V., More, K. C., Taler, J., & Ocłoń, P. (2016). Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Applied Thermal Engineering,103, 572–582. CrossRef
Trestian, R., Ormond, O., & Muntean, G. (2013). Energy–quality–cost tradeoff in a multimedia-based heterogeneous wireless network environment. IEEE Transactions on Broadcasting,59(2), 340–357. CrossRef
Trestian, R., Ormond, O., & Muntean, G. (2016). Performance evaluation of MADM-based methods for network selection in a multimedia wireless environment. Wireless Networks,21(5), 1745–1763. CrossRef
Meenakshi, M., & Singh, N. P. (2016). A comparative study of cooperative and non-cooperative game theory in network selection. In IEEE international conference on computational techniques in information and communication technologies (ICCTICT) (pp. 612–617).
Munjal, M., & Singh, N. P. (2016). Improved network selection for multimedia applications. Transactions on Emerging Telecommunications Technologies,28, 1–16.
Zheng, S. H. I., & Qi, Z. H. U. (2012). Network selection based on multiple attribute decision making and group decision making for heterogeneous wireless networks. The Journal of China Universities of Posts and Telecommunications,19(5), 92–98. CrossRef
Sgora, A., Gizelis, C. A., & Vergados, D. D. (2011). Network selection in a WiMAX–WiFi environment. Pervasive and Mobile Computing,7(5), 584–594. CrossRef
Kuo, Y., Yang, T., & Huang, G. W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computer and Industrial Engineering,55(1), 80–93. CrossRef
- Utility aware network selection in small cell
Niraj Pratap Singh
- Springer US
- Wireless Networks
The Journal of Mobile Communication, Computation and Information
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196