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A novel prediction model for mobility tracing of users with hybrid metaheuristic concept

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Abstract

In the modern world, it is necessary to monitor a user's mobility in everyday life to provide advanced mobile services. Mostly, the location-based services (LBS) depend on both the present and future location of the user, which shows the increasing demand on predicting the user's future location. The prediction of trajectories among two different locations is very important as it aids to optimize travel paths among the locations. Moreover, existing techniques rely on high-quality data to deliver optimal results. In this research, we proposed a new optimized deep learning architecture for mobility tracing. The prediction model is based on Optimized DCNN (Deep Conventional Neural Network) that is already trained with the traced location of the user and from the user’s trained movement the model can predict its next location. For precise prediction, the weight and activation function of DCNN is optimally selected with the aid of HS-EH (Hybrid SeaLion -Elephant Herding) algorithm, which is a hybridization of Sea lion Optimization (SLnO) and Elephant Herding Optimization (EHO). Eventually, the proposed method performance is executed in MATLAB 2019a and compared with state-of-art methods with certain performance metrics. Especially, the accuracy of the proposed DCNN + HS-EH algorithm at training rate 10 is 82.96%, 48.88%, 79.27%, and 61.67% better than existing methods.

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Abbreviations

AD:

Autonomous driving

BM:

BookMaker informedness

CLTA:

Cooperative localization and tracking algorithm

3D:

Three dimension

DCNN:

Deep conventional neural network

DMMSD:

Dynamic model based mean state detection

EE:

Energy efficiency

EHO:

Elephant herding optimization

FDR:

False discovery rate

FNR:

False negative rate

FPR:

False positive rate

FOR:

False omission rate

GSTF:

Group sparsity tensor factorization

GPS:

Global positioning system

HS-EH:

Hybrid sealion-elephant herding

ICP:

Iterative closest point

ITS:

Intelligent transportation system

IMU:

Inertial measurement unit

IoT:

Internet of things

LBS:

Location-based service

MCC:

Matthews correlation coefficient

MK:

Markedness

SMS:

Short message service

SLnO:

SeaLion optimization

WSN:

Wireless sensor network

WWW:

World wide web

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Ajinu, A., Maheswaran, C.P. A novel prediction model for mobility tracing of users with hybrid metaheuristic concept. Wireless Netw 28, 107–123 (2022). https://doi.org/10.1007/s11276-021-02806-9

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