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Support Vector Regressive Dragonfly Optimized Shift Invariant Deep Neural Learning Based Handover for Seamless Data Delivery in Heterogeneous Network


Affiliations
1 Department of Computer Applications, T. John College, Bangalore, Karnataka, India
2 Department of Computer Science, Periyar University, Salem, Tamil Nadu, India
 

In a Wireless Sensor Network (WSN), seamless mobility management can change the current mobile node’s location point to another network devoid of any link failure during communication. The seamless mobility system is very useful to detect the nearest base station over the wireless network without any distinct interference. In this paper, a novel technique called Support Vector Regressive Dragonfly Optimization based Shift Invariant Deep Neural Learning (SVRDO-SIDNL) is introduced for improving the seamless data transmission with minimum delay. The Shift Invariant Deep Neural Learning comprises of many layers to learn the series of input. For each layer, the different processes are carried out to accomplish the traffic optimized seamless data delivery. The input layer of the deep neural learning receives mobile nodes with coverage region and then is sent to the hidden layer. The mobile nodes' signal strength is analyzed by applying the support vector regression at the hidden layer. Then, the node with weak signal strength is identified and performs the handover. Through oppositional learned multi-objective dragonfly optimization technique, recognition of nearby attachment points with greater bandwidth availability is performed for the handover process. Then, the mobile node connection is altered from the existing attachment point to a new attachment point without losing connectivity. The simulation results reveal that the SVRDO-SIDNL technique offers a greater delivery rate, throughput with lesser packet loss at less delay.

Keywords

Mobility Management System, Shift Invariant Deep Neural Learning, Support Vector Regression, Oppositional Learned Multi-Objective Dragonfly Optimization, Handover
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  • Meenakshi Subramani, Vinoth Babu Kumaravelu, “A Quality-Aware Fuzzy-Logic-Based Vertical Handover Decision Algorithm for Device-to-Device Communication”, Arabian Journal for Science and Engineering, Springer, Volume no. 44, 2019, Pages 2413-2425.

  • Ensar Zeljkovic, Nina Slamnik-Kriještorac, Steven Latré, and Johann M. Marquez-Barja, “ABRAHAM: Machine Learning Backed Proactive Handover Algorithm Using SDN”, IEEE Transactions on Network and Service Management, Volume no. 16, Issue no. 4, 2019, Pages 1522 – 1536.

  • Khong-Lim Yap, Yung-Wey Chong, Weixia Liu, “Enhanced handover mechanism using mobility prediction in wireless networks”, PLoS ONE, Volume no. 15, Issue no. 1, 2020, Pages 1-31.

  • Igor Bisio and Andrea Sciarrone, “Fast Multiattribute Network Selection Technique for Vertical Handover in Heterogeneous Emergency Communication Systems”, Wireless Communications and Mobile Computing, Hindawi, Volume no. 2019, April 2019, Pages 1-17.

  • Khalid M. Hosny, Marwa M. Khashaba, Walid I. Khedr, Fathy A. Amer, “New vertical handover prediction schemes for LTE-WLAN heterogeneous networks”, PLoS ONE, Volume no. 14, Issue no. 4, 2019, Pages 1-31.

  • Radhwan Mohamed Abdullah, Abedallah Zaid Abualkishik, Ali A. Alwan, “Improved Handover Decision Algorithm Using Multiple Criteria”, Procedia Computer Science, Elsevier, Volume no. 141, 2018, Pages 32-39.

  • Thiago Coqueiro, José Jailton, Tássio Carvalho, and Renato Francês, “A Fuzzy Logic System for Vertical Handover and Maximizing Battery Lifetime in Heterogeneous Wireless Multimedia Networks”, Wireless Communications and Mobile Computing, Hindawi, Volume no. 2019, January 2019, Pages 1-13.

  • Mansoor Davoodi, Esmaeil Delfaraz, Sajjad Ghobadi, and Mahtab Masoori, “Algorithms for Handoff Minimization in Wireless Networks”, Journal of computer science and technology, Springer, Volume no. 34, Issue no. 4, 2019, Pages 887-900.

  • Jianfeng Guan, Vishal Sharma, Ilsun You, Mohammed Atiquzzaman, Muhammad Imran, “Extension of MIH for FPMIPv6 (EMIH-FPMIPv6) to support optimized heterogeneous handover”, Future Generation Computer Systems, Elsevier, Volume no. 97, 2019, Pages 775-791.

  • MdMahedi Hassan, Ian K.T. Tan, Timothy TzenVun Yap, “Data of vertical and horizontal handover on video transmission in Proxy Mobile IPv6”, Data in brief, Elsevier, Volume no. 27, 2019, Pages 1-10.

  • Xiaodong Xu, Zhao Sun, Xun Dai, Tommy Svensson, and Xiaofeng Tao, “Modeling and Analyzing the Cross-Tier Handover in Heterogeneous Networks”, IEEE Transactions On Wireless Communications, Volume no. 16, Issue no. 12, 2017, Pages 7859-7869.

  • Ji-Hwan Choi and Dong-Joon Shin, “Generalized RACH-Less Handover for Seamless Mobility in 5G and Beyond Mobile Networks”, IEEE Wireless Communications Letters, Volume no. 8, Issue no. 4, 2019, Pages 1264-1267.

  • Rana R Ahmed and Demetres D Kouvatsos, “An Efficient CoMP-based Handover Scheme for Evolving Wireless Networks”, Electronic Notes in Theoretical Computer Science, Volume no. 340, 2018, pages 85-99.

  • A.M.Aibinu, A.J.Onumanyi, A.P.Adedigba, M.Ipinyomi, T.A.Folorunso, M.J.E.Salami, “Development of hybrid artificial intelligent based handover decision algorithm”, Engineering Science and Technology, an International Journal, Elsevier, Volume no. 20, Issue no. 2, April 2017, Pages 381-390.

  • Ali F. Almutairi, Mohannad Hamed, Mohamed Adnan Landolsi, Mishal Algharabally, “A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks”, Telecommunication Systems, Springer, Volume no. 68, Issue no. 2, June 2018, Pages 151–161.

  • Xiaohong Li, Feng Liu, Zhiyong Feng, Guangquan Xu, Zhangjie Fu, “A novel optimized vertical handover framework for seamless networking integration in cyber-enabled systems”, Future Generation Computer Systems, Elsevier, Volume no. 79, Part 1, 2018, Pages 417-430.

  • Mohamed Lahby, Ayoub Essouiri, Abderrahim Sekkaki, “A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks”, Digital Communications and Networks, Elsevier, Volume no. 5, Issue no. 4, 2019, Pages 297-307.

  • Shidrokh Goudarzi, Wan Haslina Hassan, Mohammad Hossein Anisi & Seyed Ahmad Soleymani, “MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks”, Wireless Personal Communications, Springer, Volume no. 92, Issue no.2, 2017,Pages 399- 436.

  • Hideo Kobayashi, Eiichi Kameda, Yoshiaki Terashima, Norihiko Shinomiya, “Towards sustainable heterogeneous wireless networks: A decision strategy for AP selection with dynamic graphs”, Computer Networks, Elsevier, Volume no. 132, 2018, Pages 99-107.

  • Raman Kumar Goyal, Sakshi Kaushal, Arun Kumar Sangaiah, “The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks”, Applied Soft Computing, Elsevier, Volume no. 67, June 2018, Pages 800-811.

  • Bilen, T., & Canberk, B. “Overcoming 5G ultra-density with game theory: Alpha-beta pruning aided conflict detection”, Pervasive and Mobile Computing, Volume no. 63, March 2020, Pages 1-41.

  • Hernández-Orallo, E., Borrego, C., Manzoni, P., Marquez-Barja, J. M., Cano, J. C., & Calafate, C. T. Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing. Pervasive and Mobile Computing, Volume no. 67, September 2020, Pages 1-18.


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  • Support Vector Regressive Dragonfly Optimized Shift Invariant Deep Neural Learning Based Handover for Seamless Data Delivery in Heterogeneous Network

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Authors

D. Somashekhara Reddy
Department of Computer Applications, T. John College, Bangalore, Karnataka, India
C. Chandrasekhar
Department of Computer Science, Periyar University, Salem, Tamil Nadu, India

Abstract


In a Wireless Sensor Network (WSN), seamless mobility management can change the current mobile node’s location point to another network devoid of any link failure during communication. The seamless mobility system is very useful to detect the nearest base station over the wireless network without any distinct interference. In this paper, a novel technique called Support Vector Regressive Dragonfly Optimization based Shift Invariant Deep Neural Learning (SVRDO-SIDNL) is introduced for improving the seamless data transmission with minimum delay. The Shift Invariant Deep Neural Learning comprises of many layers to learn the series of input. For each layer, the different processes are carried out to accomplish the traffic optimized seamless data delivery. The input layer of the deep neural learning receives mobile nodes with coverage region and then is sent to the hidden layer. The mobile nodes' signal strength is analyzed by applying the support vector regression at the hidden layer. Then, the node with weak signal strength is identified and performs the handover. Through oppositional learned multi-objective dragonfly optimization technique, recognition of nearby attachment points with greater bandwidth availability is performed for the handover process. Then, the mobile node connection is altered from the existing attachment point to a new attachment point without losing connectivity. The simulation results reveal that the SVRDO-SIDNL technique offers a greater delivery rate, throughput with lesser packet loss at less delay.

Keywords


Mobility Management System, Shift Invariant Deep Neural Learning, Support Vector Regression, Oppositional Learned Multi-Objective Dragonfly Optimization, Handover

References





DOI: https://doi.org/10.22247/ijcna%2F2020%2F202936