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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar bander alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

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Computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： bander alzahrani Ahmed Barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial *** example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real ***,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management *** addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication ***,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD ***,the AKA scheme involves many vulnerabilities in terms of security and *** this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling *** scheme supports key security features,including anti-forgery attacks,and confirms user *** security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle *** simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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Network Learning-Enabled Sensor Association for Massive Internet of Things

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Computer Systems Science & Engineering 2023年第10期47卷 843-853页

作者： Alaa Omran Almagrabi Rashid Ali Daniyal Alghazzawi bander A.alzahrani Fahad M.Alotaibi Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information and Communication Technologies Universitat Pompeu FabraBarcelona08018Spain

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with *** IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single *** is mitigated by allotting various channels to adjoining ***,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network *** an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and ***,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML ***,this paper proposes the use of an RL algorithm for GW determination and association in IoT *** this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to *** this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network *** evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

关键词： Reinforcement learning association internet of things massive IoT sensors network

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