Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

Yun, Won Joon; Park, Soo-Hyun; Kim, Joongheon; Shin, MyungJae; Jung, Soyi; Mohaisen, Aziz; Kim, Jae Hyun

doi:10.1109/tii.2022.3143175

Cited by 87 publications

(34 citation statements)

References 24 publications

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“…The UAV's active beamforming, RIS elements' coefficients, and UAV's trajectory were jointly optimized to maximize the sum secrecy rate of the legitimate users in the presence of multiple attackers. The work in [187] has introduced a multi-agent DRL-based management scheme to minimize the UAVs' overlap and shadowed regions for reliable and flexible UAVs-assisted surveillance services over a large area. The authors in [188] have investigated a distributed RL-based energy-constrained UAV relay network under a jamming attack in which the locations of both UAV and jammer are unknown.…”

Section: A Secured Uav Communications Using Machine-learningmentioning

confidence: 99%

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

et al. 2022

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Unmanned aerial vehicles (UAVs) have been instrumental in enabling many new applications and services, including military and rescue operations, aerial surveillance, civilian applications, precision farming, as well as providing extensive wireless network access in remote areas. They have also been used in other areas such as transmission line and oil rig monitoring, disaster recovery, etc. With their increasing payload ability and flight duration, using UAVs is a preferred choice for a multitude of upcoming wireless communication systems. However, because of their open operational nature, UAVs are highly vulnerable to severe security breaches via cyber attacks, eavesdropping on navigational and communication links, etc. Given their widespread applications, the requirement of secured UAV communications has become more and more critical since security failures can lead to detrimental consequences. Several works have examined the extent of privacy and security issues in UAV-assisted networks and introduced various mitigation techniques to address different security challenges. In this paper, a comprehensive survey is conducted that centers on the security issues related to UAV-aided networks. A descriptive taxonomy of various security intrusions on the UAV networks and commonly-used secrecy performance metrics are thoroughly reviewed. An in-depth discussion is provided on alleviating threats with proactive security techniques blended with key wireless communication technologies such as mmWave, non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and cognitive radio. Moreover, several emerging topics like machine learning, software-defined networks, fog and edge computing, blockchain, are discussed in the context of UAV-aided secure communications.INDEX TERMS Physical layer security, unmanned aerial vehicles (UAVs), proactive mitigation techniques, secrecy metrics, emerging wireless technologies.GAURAV KUMAR PANDEY (Graduate Student Member, IEEE) received the B.Tech. degree in electrical and electronics engineering from the

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Section: A Secured Uav Communications Using Machine-learningmentioning

confidence: 99%

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

et al. 2022

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“…The experiment shows that when the deterrent separation is 1280 meters, 20 UAVs can discourage virtually all offenses. To provide a reliable surveillance system using a swarm of UAVs, a collaborative model-free multi-agent deep reinforcement learning-based algorithm has been proposed in the literature [82], which finds the optimal trajectory within the surveillance area in order to optimize the energy consumption and the number of users that can be monitored.…”

Section: Surveillancementioning

confidence: 99%

AI for UAV-Assisted IoT Applications : A Comprehensive Review

Chen¹,

Wu²,

Wang³

et al. 2022

Preprint

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With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

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“… Yun et al (2022) incorporated an actor–critic method called CommNet for the deployment of CCTV-equipped multi-UAVs with a focus on autonomous network recovery to ensure reliable industry surveillance. As mobile CCTV UAVs can continuously move over a wide area, they provide a robust solution for surveillance in dynamic manufacturing environments.…”

Section: Applicationsmentioning

confidence: 99%

A review of the applications of multi-agent reinforcement learning in smart factories

Bahrpeyma

Reichelt

2022

Front. Robot. AI

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The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing advanced manufacturing systems and realizing modern manufacturing objectives such as mass customization, automation, efficiency, and self-organization all at once. Such manufacturing systems, however, are characterized by dynamic and complex environments where a large number of decisions should be made for smart components such as production machines and the material handling system in a real-time and optimal manner. AI offers key intelligent control approaches in order to realize efficiency, agility, and automation all at once. One of the most challenging problems faced in this regard is uncertainty, meaning that due to the dynamic nature of the smart manufacturing environments, sudden seen or unseen events occur that should be handled in real-time. Due to the complexity and high-dimensionality of smart factories, it is not possible to predict all the possible events or prepare appropriate scenarios to respond. Reinforcement learning is an AI technique that provides the intelligent control processes needed to deal with such uncertainties. Due to the distributed nature of smart factories and the presence of multiple decision-making components, multi-agent reinforcement learning (MARL) should be incorporated instead of single-agent reinforcement learning (SARL), which, due to the complexities involved in the development process, has attracted less attention. In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting smart factory attributes to the equivalent MARL features, based on which we suggest MARL to be one of the most effective approaches for implementing the control mechanism for smart factories.

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Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

Cited by 87 publications

References 24 publications

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

AI for UAV-Assisted IoT Applications : A Comprehensive Review

A review of the applications of multi-agent reinforcement learning in smart factories

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