Mobility Prediction of Mobile Wireless Nodes
Abstract
:1. Introduction and Motivation
2. Mobility Models
2.1. Gauss–Markov
2.2. Random Waypoint
2.3. Random Walk
2.4. Random Direction
2.5. RSSGM
3. Machine Learning Classifiers
3.1. Logistic Regression
3.2. Decision Tree
3.3. K-Nearest Neighbors
3.4. Latent Dirichlet Allocation
3.5. Gaussian Naive Bayes
3.6. Support Vector Machines
4. Related Works
5. Methodology
6. Results and Evaluation
6.1. Evaluation of the RD Mobility Model
6.2. Evaluation of the RW Mobility Model
6.3. Evaluation of the Gauss–Markov Mobility Model
6.4. Evaluation of the RSSGM Mobility Model
7. Discussion and Analysis
8. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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APs | X (m) | Y (m) | Centroids | X (m) | Y (m) |
---|---|---|---|---|---|
1 | −800 | −200 | 1 | −400 | −700 |
2 | −400 | 700 | 2 | −600 | 900 |
3 | 0 | 100 | 3 | 100 | 800 |
4 | 500 | 900 | 4 | 700 | 500 |
5 | 500 | −800 | 5 | 500 | −400 |
6 | −500 | −900 | 6 | −700 | −500 |
7 | −450 | −350 | 7 | −500 | 100 |
8 | −100 | −600 | 8 | 300 | 400 |
9 | −600 | 300 | 9 | 800 | 0 |
10 | −800 | 850 | 10 | 200 | −600 |
11 | −100 | 950 | 11 | −200 | −200 |
12 | 900 | 600 | 12 | −800 | 400 |
13 | 500 | 400 | 13 | 800 | −800 |
14 | 850 | 250 | 14 | −100 | 500 |
15 | 300 | −200 | 15 | 200 | 0 |
16 | 200 | −850 | 16 | −900 | −900 |
17 | 700 | −500 | 17 | −100 | −900 |
18 | −300 | 0 | 18 | 500 | 100 |
19 | 100 | 550 | 19 | 900 | −400 |
20 | −900 | −700 | 20 | −900 | 700 |
Time (s) | X (m) | Y (m) | Previous AP | Current AP |
---|---|---|---|---|
32 | −522.039 | 234.0297 | 0 | 1 |
33 | −534.758 | 282.532 | 1 | 1 |
34 | −545.7 | 331.5814 | 1 | 1 |
35 | −551.134 | 381.5525 | 1 | 1 |
36 | −560.975 | 430.9473 | 1 | 1 |
. | ||||
. | ||||
. | ||||
467 | 149.2532 | 660.1039 | 2 | 3 |
468 | 114.1719 | 625.27 | 3 | 3 |
469 | 76.80538 | 593.0601 | 3 | 1 |
470 | 39.04904 | 561.3788 | 1 | 1 |
471 | 5.846693 | 524.811 | 1 | 2 |
472 | −25.0253 | 486.2131 | 2 | 2 |
473 | −18.7087 | 535.4928 | 2 | 1 |
474 | −1.68525 | 582.249 | 1 | 1 |
475 | 23.00982 | 625.3539 | 1 | 1 |
476 | 45.7389 | 669.5808 | 1 | 1 |
477 | 58.65014 | 717.8236 | 1 | 1 |
478 | 75.00265 | 764.9371 | 1 | 3 |
479 | 86.98774 | 813.4452 | 3 | 3 |
480 | 132.711 | 793.1574 | 3 | 3 |
. | ||||
. | ||||
. | ||||
799 | 680.0722 | 395.8155 | 2 | 3 |
800 | 628.7113 | 401.9032 | 3 | 3 |
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Abbas, S.; Alenazi, M.J.F.; Samha, A. Mobility Prediction of Mobile Wireless Nodes. Appl. Sci. 2022, 12, 13041. https://doi.org/10.3390/app122413041
Abbas S, Alenazi MJF, Samha A. Mobility Prediction of Mobile Wireless Nodes. Applied Sciences. 2022; 12(24):13041. https://doi.org/10.3390/app122413041
Chicago/Turabian StyleAbbas, Shatha, Mohammed J. F. Alenazi, and Amani Samha. 2022. "Mobility Prediction of Mobile Wireless Nodes" Applied Sciences 12, no. 24: 13041. https://doi.org/10.3390/app122413041
APA StyleAbbas, S., Alenazi, M. J. F., & Samha, A. (2022). Mobility Prediction of Mobile Wireless Nodes. Applied Sciences, 12(24), 13041. https://doi.org/10.3390/app122413041