Bagging Ensembled Perceptron Classifier for Seamless Mobility System in Heterogeneous Network
D. Somashekhara Reddy1, Chandrasekhar2
1D.Somashekhara Reddy*, Assistant Professor, JOHN College, Bangalore, India.
2Dr. Chandrasekhar, Professor, Department of Computer Science, Periyar University, Selam, Tamil Nadu, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 28, 2020. | Manuscript published on February 10, 2020. | PP: 567-574 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1378029420/2020©BEIESP | DOI: 10.35940/ijitee.D1378.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless mobile devices require a handover decision system to get a seamless connection in a heterogeneous wireless networking environment. The handover process is one of the most significant processes in a cellular network. Few research works have been developed for providing seamless connectivity using different handover techniques. But, controlling data traffic during the process of seamless mobile data connectivity was not solved. So, there is a necessity to introduce a new model to control the traffic and improving the seamless mobility management in heterogeneous network. A new model called Bagging Ensembled Perceptron Classification based Seamless Mobility (BEPC-SM) introduced to achieve higher data delivery rate with minimum packet loss rate and data transmission delay by means of classifying the mobile nodes in heterogeneous network. In BEPC-SM model, randomly considers a number of mobile nodes in the heterogeneous network as input. Then, BEPC-SM model determines signal strength for each mobile node in a heterogeneous network. Bagging Ensembled Perceptron Classification algorithm is used in BEPC-SM model with the aim of accurately classifying all mobile nodes as strong or weak strength node with a lower amount of time consumption. After that, the distance between the weak strength node and the access point in the network is measured. Lastly, BEPC-SM Model selects the nearby access point with maximum bandwidth availability for each weak strength node in the network to perform the handover process. Thus, the performance of seamless data communication in a heterogeneous network is improved in BEPC-SM model. The BEPC-SM model is used in traffic-aware seamless data communication in a heterogeneous network. Simulation evaluation of the BEPC-SM Model is carried out on factors such as data delivery rate, packet loss rate, data transmission delay with respect to a number of data packets. The simulation result depicts that the BEPC-SM Model is able to increases the data delivery rate and also reduces delay when compared to state-of-the-art works.
Keywords: Handover, Seamless Connectivity, Traffic control, Bagging Ensembled Perceptron classifier
Scope of the Article: Network traffic Characterization and Measurements