Prediction of Vertical Handover Using Multivariate Regression Model
Siddharth Goutam1, Srija Unnikrishnan2, Sundary Prabavathy3, Neel Kudu4

1Siddharth Goutam, Electronics Engineering, Fr. Conceicao Rodrigues College of Engineering, Bandra-West Mumbai,
2Dr. Srija Unnikrishnan, Principal, Fr. Conceicao Rodrigues College of Engineering, Bandra-West Mumbai, India.
3Sundary Prabavathy, Associate Professor Mathematics,Fr. Conceicao Rodrigues College of Engineering, Bandra-West Mumbai, India Neel
4Kudu, B.E Computer Engineering,

Manuscript received on 15 July 2019 | Revised Manuscript received on 20 July 2019 | Manuscript published on 30 August 2019 | PP: 2626-2633 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93630881019/19©BEIESP | DOI: 10.35940/ijitee.J9363.0881019
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Abstract: Choosing the best performing network among the pool of available networks is a challenge faced by every mobile user. This paper majorly focuses on the development of a system that can ease the above task based on available parameters like Received Signal Strength, Bandwidth, Cost, Network Coverage, Jitter, Packet Loss, Latency etc. The system uses Multivariate Regression Model to help select the best network with the help of a mobile application. The algorithm assigns a Handover value to each available network among the pool of available networks and makes a comparative study to facilitate the decision of the best performing network. The system can also be used by service providers to optimise their network performance by studying the effect of change in the network parameters. Another possible application can be to check the Handover feasibility of future technology networks. The paper also tries to focus on the giving insights on the influence each network parameter mentioned above has on the final value of Handover by using statistical methods and ANOVA.
Keywords: Vertical Handover (VHO), Handover Value (HV), Received Signal Strength (RSS), Quality of Service (QoS) Analysis of Variance (ANOVA), Multivariate Linear Regression (MLR), Coefficient of Correlation, Coefficient of Regression
Scope of the Article: Regression and Prediction