TY  - JOUR
AU  - Abro, Ghulam E Mustafa
AU  - Ali, Zain Anwar
AU  - Abdallah, Ayman M
PY  - 2024
DA  - 2024/09/23
TI  - Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 2
SP  - 69
EP  - 78
DO  - 10.62762/TIS.2024.807714
UR  - https://www.icck.org/article/abs/TIS.2024.807714
KW  - autonomous drone swarms
KW  - RSSI
KW  - indoor security
KW  - unmanned aerial vehicle (UAVs) and mitigation
AB  - A Novel simulation framework using autonomous 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 contain it for 20 seconds, rendering it immobilized, before the swarm returns to its original formation. 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 critical environments such as warehouses, laboratories, and other secure indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Abro2024Signal,
  author = {Ghulam E Mustafa Abro and Zain Anwar Ali and Ayman M Abdallah},
  title = {Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {69-78},
  doi = {10.62762/TIS.2024.807714},
  url = {https://www.icck.org/article/abs/TIS.2024.807714},
  abstract = {A Novel simulation framework using autonomous 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 contain it for 20 seconds, rendering it immobilized, before the swarm returns to its original formation. 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 critical environments such as warehouses, laboratories, and other secure indoor facilities. This study advances unmanned aerial system (UAS) autonomous swarm intelligence and security procedures.},
  keywords = {autonomous drone swarms, RSSI, indoor security, unmanned aerial vehicle (UAVs) and mitigation},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}