Optimizing drone-assisted victim localization and identification in mass-disaster management: a study on feasible flying patterns and technical specifications
Azmi I.N. Kassim M. Yussoff Y.M. Tahir N.M.
August 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 44097 - 4109 pp.
The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions.
Drone-assisted , Flying-pattern , Mass-disaster management , Search and rescue , Victim identification
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School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia
Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA, Shah Alam, Malaysia
Information Security and Trusted Infrastructure Laboratory, Universiti Teknologi MARA, Shah Alam, Malaysia
Department of Cybersecurity, International Information Technology University, Almaty, Kazakhstan
Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
School of Electrical Engineering
Institute for Big Data Analytics and Artificial Intelligence
Information Security and Trusted Infrastructure Laboratory
Department of Cybersecurity
Applied College
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