Indoor Localization Using Artificial Bee Colony with Levy Flight

Öz

Using Wi-Fi signal strength for detecting objects in an indoor environment has different types of applications, such as, locating perpetrator in finite areas, and detecting the number of users on an access point. In this work, we propose a hybrid optimization algorithm to train training Multi-Layer Perceptron Neural Network that could be distributed in monitoring and tracking devices used for determining the location of users based on the Wi-Fi signal strength which their personal devices receive. This hybrid algorithm combines Artificial Bee Colony (ABC) and Levy Flight (LF) algorithm, called ABCLF. ABCLF increases the exploration and exploitation capabilities of ABC so that it can be used efficiency for the purpose of training Multi-Layer Perceptron “MLP” Neural Network. The proposed ABCLF algorithm guarantees the enhancing of accuracy with the increasing in iterations because it has the powerfull of the frame work of Artificial Bee Colony algorithm “three phases whith different strategies of searching” and the powerfull of the Levy Flight local search algorithm which has been used in both onlooker bee phase with a short step size walk to guarantee the enhancing in the exploitation and in scout bee phase with a long step size walk to guarantee the enhancing in the exploration. The results of our study show that the classification accuracy of the trained neural network using ABCLF is better than the other evolutionary algorithms used in this study for the same purpose like ABC, Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Probability Based Incremental Learning (PBIL) and Particle Swarm Optimization (PSO).

Anahtar Kelimeler

• Indoor localization
• artificial bee colony
• levy flight
• neural network
• metaheuristics
• multi-layer perceptron

Indoor Localization Using Artificial Bee Colony with Levy Flight

Abstract

Using Wi-Fi signal strength for detecting objects in an indoor environment has different types of applications, such as, locating perpetrator in finite areas, and detecting the number of users on an access point. In this work, we propose a hybrid optimization algorithm to train training Multi-Layer Perceptron Neural Network that could be distributed in monitoring and tracking devices used for determining the location of users based on the Wi-Fi signal strength which their personal devices receive. This hybrid algorithm combines Artificial Bee Colony (ABC) and Levy Flight (LF) algorithm, called ABCLF. ABCLF increases the exploration and exploitation capabilities of ABC so that it can be used efficiency for the purpose of training Multi-Layer Perceptron “MLP” Neural Network. The proposed ABCLF algorithm guarantees the enhancing of accuracy with the increasing in iterations because it has the powerfull of the frame work of Artificial Bee Colony algorithm “three phases whith different strategies of searching” and the powerfull of the Levy Flight local search algorithm which has been used in both onlooker bee phase with a short step size walk to guarantee the enhancing in the exploitation and in scout bee phase with a long step size walk to guarantee the enhancing in the exploration. The results of our study show that the classification accuracy of the trained neural network using ABCLF is better than the other evolutionary algorithms used in this study for the same purpose like ABC, Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Probability Based Incremental Learning (PBIL) and Particle Swarm Optimization (PSO).

Keywords

• Indoor localization
• artificial bee colony
• levy flight
• neural network
• metaheuristics
• multi-layer perceptron

References

1. Rohra, J. G., Perumal, B., Narayanan, S. J., Thakur, P., & Bhatt, R. B. (2017). User localization in an indoor environment using fuzzy hybrid of particle swarm optimization & gravitational search algorithm with neural networks. In Proceedings of Sixth International Conference on Soft Computing for Problem Solving (pp. 286-295). Springer, Singapore.
2. Feng, C., Au, W. S. A., Valaee, S., & Tan, Z. (2011). Received-signal-strength-based indoor positioning using compressive sensing. IEEE Transactions on mobile computing, 11(12), 1983-1993.‏
3. Pivato, P., Palopoli, L., & Petri, D. (2011). Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Transactions on Instrumentation and Measurement, 60(10), 3451-3460.
4. Bahl, P., Padmanabhan, V. N., Bahl, V., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and tracking system.‏
5. Bahl, V., & Padmanabhan, V. (2000). Enhancements to the RADAR user location and tracking system.‏
6. Lien, L. C., & Cheng, M. Y. (2012). A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert Systems with Applications, 39(10), 9642-9650.
7. Chen, S., Liu, Y., Wei, L., & Guan, B. (2018). PS-FW: a hybrid algorithm based on particle swarm and fireworks for global optimization. Computational intelligence and neuroscience, 2018.‏
8. Tsai, P. W., Pan, J. S., Shi, P., & Liao, B. Y. (2011). A new framework for optimization based-on hybrid swarm intelligence. In Handbook of Swarm Intelligence (pp. 421-449). Springer, Berlin, Heidelberg.‏

9. Jadon, S. S., Tiwari, R., Sharma, H., & Bansal, J. C. (2017). Hybrid artificial bee colony algorithm with differential evolution. Applied Soft Computing, 58, 11-24.‏
10. Li, X., Peng, Z., Du, B., Guo, J., Xu, W., & Zhuang, K. (2017). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113, 10-26.
11. Chengli, F. A. N., Qiang, F. U., Guangzheng, L. O. N. G., & Qinghua, X. I. N. G. (2018). Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism. Journal of Systems Engineering and Electronics, 29(2), 405-414.‏
12. Sharma, H., Bansal, J. C., Arya, K. V., & Yang, X. S. (2016). Lévy flight artificial bee colony algorithm. International Journal of Systems Science, 47(11), 2652-2670.‏
13. Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied mathematics and computation, 217(7), 3166-3173.
14. Sharma, H., Bansal, J. C., & Arya, K. V. (2013). Opposition based lévy flight artificial bee colony. Memetic Computing, 5(3), 213-227.
15. Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.