Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems
Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas w...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/6/9/222 |
| _version_ | 1797489276751970304 |
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| author | Fatma S. Alrayes Saud S. Alotaibi Khalid A. Alissa Mashael Maashi Areej Alhogail Najm Alotaibi Heba Mohsen Abdelwahed Motwakel |
| author_facet | Fatma S. Alrayes Saud S. Alotaibi Khalid A. Alissa Mashael Maashi Areej Alhogail Najm Alotaibi Heba Mohsen Abdelwahed Motwakel |
| author_sort | Fatma S. Alrayes |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures. |
| first_indexed | 2024-03-10T00:15:10Z |
| format | Article |
| id | doaj.art-97cb0b56ba9844c19b989fe503757439 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T00:15:10Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-97cb0b56ba9844c19b989fe5037574392023-11-23T15:53:27ZengMDPI AGDrones2504-446X2022-08-016922210.3390/drones6090222Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring SystemsFatma S. Alrayes0Saud S. Alotaibi1Khalid A. Alissa2Mashael Maashi3Areej Alhogail4Najm Alotaibi5Heba Mohsen6Abdelwahed Motwakel7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, P.O. Box 715, Mecca 24381, Saudi ArabiaNetworks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi ArabiaPrince Saud AlFaisal Institute for Diplomatic Studies, P.O. Box 51988, Riyadh 11553, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj 16278, Saudi ArabiaUnmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.https://www.mdpi.com/2504-446X/6/9/222securityimage encryptionemergency monitoring systemdronesdata classificationdeep learning |
| spellingShingle | Fatma S. Alrayes Saud S. Alotaibi Khalid A. Alissa Mashael Maashi Areej Alhogail Najm Alotaibi Heba Mohsen Abdelwahed Motwakel Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems Drones security image encryption emergency monitoring system drones data classification deep learning |
| title | Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems |
| title_full | Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems |
| title_fullStr | Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems |
| title_full_unstemmed | Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems |
| title_short | Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems |
| title_sort | artificial intelligence based secure communication and classification for drone enabled emergency monitoring systems |
| topic | security image encryption emergency monitoring system drones data classification deep learning |
| url | https://www.mdpi.com/2504-446X/6/9/222 |
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