Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems

被引:21
作者
Alrayes, Fatma S. [1 ]
Alotaibi, Saud S. [2 ]
Alissa, Khalid A. [3 ]
Maashi, Mashael [4 ]
Alhogail, Areej [5 ]
Alotaibi, Najm [6 ]
Mohsen, Heba [7 ]
Motwakel, Abdelwahed [8 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Dept Informat Syst, Coll Comp & Informat Sci, POB 84428, Riyadh 11671, Saudi Arabia
[2] Umm Al Qura Univ, Dept Informat Syst, Coll Comp & Informat Syst, POB 715, Mecca 24381, Saudi Arabia
[3] Imam Abdulrahman Bin Faisal Univ, Networks & Commun Dept, Coll Comp Sci & Informat Technol, POB 1982, Dammam 31441, Saudi Arabia
[4] King Saud Univ, Dept Software Engn, Coll Comp & Informat Sci, POB 103786, Riyadh 11543, Saudi Arabia
[5] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 103786, Riyadh 11543, Saudi Arabia
[6] Prince Saud AlFaisal Inst Diplomat Studies, POB 51988, Riyadh 11553, Saudi Arabia
[7] Future Univ Egypt, Fac Comp & Informat Technol, Dept Comp Sci, New Cairo 11835, Egypt
[8] Prince Sattam bin Abdulaziz Univ, Dept Comp & Self Dev, Preparatory Year Deanship, Alkharj 16278, Saudi Arabia
关键词
security; image encryption; emergency monitoring system; drones; data classification; deep learning; IMAGE CLASSIFICATION; INTERNET; SCHEME;
D O I
10.3390/drones6090222
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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.
引用
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页数:18
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