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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DOI: https://doi.org/10.1007/s11276-021-02806-9