-
CiteScore
1.08
Impact Factor
IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 58-67

Free Access | Research Article | 23 September 2024
1 Interdisciplinary Research Centre for Aviation and Space Exploration (IRCASE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Kingdom of Saudi Arabia
2 Electronic Engineering Department, Maynooth International Engineering College (MIEC), Maynooth University, Maynooth, Co. Kildare, Ireland
3 Aerospace Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Kingdom of Saudi Arabia
* Corresponding author: Ghulam E Mustafa Abro, email: mustafa.abro@ieee.org
Received: 13 september 2024, Accepted: 21 September 2024, Published: 23 September 2024  

Abstract
A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulation results show that the approach works in delicate environments including warehouses, laboratories, and other indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.

Graphical Abstract
Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations

Keywords
Autonomous Drone Swarms
RSSI
Indoor Security
Unmanned Aerial Vehicle (UAVs) and Mitigation

References

[1] Obaid, M., Johal, W., & Mubin, O. (2020, November). Domestic drones: Context of use in research literature. In Proceedings of the 8th International Conference on Human-Agent Interaction (pp. 196-203).

[2] Hsieh, H. C., Jan, G. E., & Luo, H. L. (2023, November). The Applications and Presentations of Drones in Staged Performances and Contemporary Art. In 2023 IEEE International Conference on e-Business Engineering (ICEBE) (pp. 281-286). IEEE.

[3] Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., ... & Burschka, D. (2012). Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE robotics & automation magazine, 19(3), 46-56.

[4] Famili, A., Stavrou, A., Wang, H., & Park, J. M. (2022). Pilot: High-precision indoor localization for autonomous drones. IEEE Transactions on Vehicular Technology, 72(5), 6445-6459.

[5] Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., ... & Guizani, M. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572-48634.

[6] Qu, C., Boubin, J., Gafurov, D., Zhou, J., Aloysius, N., Nguyen, H., & Calyam, P. (2022, October). Uav swarms in smart agriculture: Experiences and opportunities. In 2022 IEEE 18th International Conference on e-Science (e-Science) (pp. 148-158). IEEE.

[7] Ming, R., Jiang, R., Luo, H., Lai, T., Guo, E., & Zhou, Z. (2023). Comparative analysis of different uav swarm control methods on unmanned farms. Agronomy, 13(10), 2499.

[8] Fang, Z., & Savkin, A. V. (2024). Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review. Drones, 8(5), 193.

[9] Aslan, M. F., Durdu, A., Yusefi, A., & Yilmaz, A. (2022). HVIOnet: A deep learning based hybrid visual–inertial odometry approach for unmanned aerial system position estimation. Neural Networks, 155, 461-474.

[10] Coppola, M., McGuire, K. N., De Wagter, C., & De Croon, G. C. (2020). A survey on swarming with micro air vehicles: Fundamental challenges and constraints. Frontiers in Robotics and AI, 7, 18.

[11] Xiaoning, Z. (2020, November). Analysis of military application of UAV swarm technology. In 2020 3rd International Conference on Unmanned Systems (ICUS) (pp. 1200-1204). IEEE.

[12] W., Hoebeke, J., & De Poorter, E. (2021). Indoor drone Vanhie-Van Gerwen, J., Geebelen, K., Wan, J., Joseph, positioning: Accuracy and cost trade-off for sensor fusion. IEEE Transactions on Vehicular Technology, 71(1), 961-974.

[13] Pérez Rubio, M. D. C., Gualda Gómez, D., Vicente Ranera, J. D., Villadangos Carrizo, J. M., & Ureña Ureña, J. (2019). Review of UAV positioning in indoor environments and new proposal based on US measurements.

[14] Harbaoui, N., Makkawi, K., Ait-Tmazirte, N., & El Najjar, M. E. B. (2024). Context Adaptive Fault Tolerant Multi-sensor fusion: Towards a Fail-Safe Multi Operational Objective Vehicle Localization. Journal of Intelligent & Robotic Systems, 110(1), 26.

[15] Youn, W., Ko, H., Choi, H., Choi, I., Baek, J. H., & Myung, H. (2021). Collision-free autonomous navigation of a small UAV using low-cost sensors in GPS-denied environments. International Journal of Control, Automation and Systems, 19(2), 953-968.

[16] Famili, A., & Park, J. M. J. (2020, May). ROLATIN: Robust localization and tracking for indoor navigation of drones. In 2020 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.

[17] Safaei, A., & Sharf, I. (2021, June). Velocity estimation for UAVs using ultra wide-band system. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 202-209). IEEE.

[18] Sun, Y., Wang, W., Mottola, L., Zhang, J., Wang, R., & He, Y. (2023). Indoor drone localization and tracking based on acoustic inertial measurement. IEEE Transactions on Mobile Computing.

[19] Ouyang, Q., Wu, Z., Cong, Y., & Wang, Z. (2023). Formation control of unmanned aerial vehicle swarms: A comprehensive review. Asian Journal of Control, 25(1), 570-593.

[20] Horyna, J., Baca, T., Walter, V., Albani, D., Hert, D., Ferrante, E., & Saska, M. (2023). Decentralized swarms of unmanned aerial vehicles for search and rescue operations without explicit communication. Autonomous Robots, 47(1), 77-93.

[21] DURDU, A., & KAYABAŞI, A. (2024). Consensus-based virtual leader tracking algorithm for flight formation control of swarm UAVs. Turkish Journal of Electrical Engineering and Computer Sciences, 32(2), 251-267.


Cite This Article
APA Style
Abro, G., E., M., Ali, Z., A. & Abdallah, A., M. (2024). Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations. IECE Transactions on Intelligent Systematics, 1(2), 58–67. https://doi.org/10.62762/TIS.2024.807714

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 312
PDF Downloads: 38

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Intelligent Systematics

IECE Transactions on Intelligent Systematics

ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)

Email: jinxuebo@btbu.edu.cn

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.