A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

Khalil, Hisham; Rahman, Saeed Ur; Ullah, Inam; Khan, Inayat; Alghadhban, Abdulaziz Jarallah; Al-Adhaileh, Mosleh Hmoud; Ali, Gauhar; ElAffendi, Mohammed

doi:10.3390/drones6120372

Cited by 29 publications

(16 citation statements)

References 36 publications

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“…Man-in-the-middle and denial-of-service attacks are only two examples of the multiple ways the transport layer of a network can be used. An attack that prevents user’s access to the targeted system, device, or network resource is known as a denial-of-service attack [ 146 ]. A cyberattack known as “man-in-the-middle” occurs when an attacker pretends to be a third party and transmits and detects messages between two objectives who believe they are speaking directly to one another.…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

Self Cite

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“…Rahman et al [42] dealt with the issue of communication among swarms of UAVs during search-and-rescue operations using methods based on machine learning. They introduced a technique for determining path loss in UAV swarms using machine learning, and it also forecasts the strength of received signals.…”

Section: F Swarm Intelligencementioning

confidence: 99%

“…Monitoring air quality [41] Using Federated Learning in a swarm of UAVs to monitor and estimate air quality of an area Lack of consistent connections between the UAV swarm and ground-based stations [27] Using distributed federated learning (FL) algorithms to improve connections between the UAV swarm and ground-based stations and enable joint power allocation Communication among swarms of UAVs during search-and-rescue operations [42] Using machine learning for determining path loss in UAV swarms, and also forecast the strength of received signals…”

Section: Swarm Intelligencementioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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“…In recent years, with the increase in mission complexity and the enhancement of UAV autonomy, unmanned aircraft swarm systems (UAS) have gradually received attention from researchers. Drone swarms have been used for target search and rescue [1,2], cluster operation [3,4], and target tracking [5,6]. Within the military domain, coordinated reconnaissance search attacks by UAV swarms in unknown environments is an important application.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

Cited by 29 publications

References 36 publications

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

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