Futuristic view of the Internet of Quantum Drones: Review, challenges and research agenda

Kumar, Adarsh; Pacheco, Diego Augusto de Jesus; Kaushik, Keshav; Rodrigues, Joel J. P. C.

doi:10.1016/j.vehcom.2022.100487

Cited by 53 publications

(20 citation statements)

References 159 publications

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“…In this prior work, interference was considered to distort the anticipated signals in a disruptive manner and to harm performance. This negative interference has been investigated by Kumar et al [ 20 ], Li and Kim [ 21 ], and Uddin et al [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Kim

Santos

Nguyen

et al. 2022

Entropy

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Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature. We infer interference from predictability by first applying various machine learning methods, including deep learning, and then computing entropy to compare against interference. Our computational framework begins by building a set of computational models called double transition models from the drone movements and revealing reward distributions using inverse reinforcement learning. These reward distributions are then used to compute the entropy and interference across a variety of drone scenarios specified by combining multiple combat strategies and command styles. Our analysis confirmed that drone scenarios experienced more interference, higher performance, and higher entropy as they became more heterogeneous. However, the direction of interference (positive vs. negative) was more dependent on combinations of combat strategies and command styles than homogeneity.

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Section: Methodsmentioning

confidence: 99%

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Kim

Santos

Nguyen

et al. 2022

Entropy

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“…The endorser node is responsible for verifying the transaction's validity and authenticity and further sending the status to full nodes. Full nodes take the help of endorser nodes for verifying transactions 23,24 Consensus Layer: An important role of this consensus layer is to use standard agreement processes to accept transactions from BoT.…”

Section: Blockchain For Botmentioning

confidence: 99%

“…Full nodes take the help of endorser nodes for verifying transactions. 23,24 • Consensus Layer: An important role of this consensus layer is to use standard agreement processes to accept transactions from BoT. The nodes in the network layers receive transactions that check the validity.…”

Section: F I G U R E 2 Blockchain Based Iobt Modelmentioning

confidence: 99%

Role of cybersecurity and Blockchain in battlefield of things

Sharma

Kumar

2023

Internet Technology Letters

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The Internet of Things is an essential component in the growth of an ecosystem that enables quick and precise judgments to be made for communication on the battleground. The usage of the battlefield of things (BoT) is, however, subject to several restrictions for a variety of reasons. There is a potential for instances of replay, data manipulation, breaches of privacy, and other similar occurrences. As a direct result of this, the implementation of a security mechanism to protect the communication that occurs within BoT has turned into an absolute requirement. To this aim, we propose a blockchain-based solution that is both safe and private for use in communications inside the BoT ecosystem. In addition, research is conducted on the benefits of integrating blockchain technology and cybersecurity into BoT application implementations. This work elaborates on the importance of integrating cybersecurity and blockchain-based tools, techniques and methodologies for BoT.

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“…However, there are several issues in drone-based quantum computing in which DRL can be utilized. For example, DRL can be used to address the problem of loss of coherence among qubits or in the allocation of communication resources among quantum drones, which are promising research directions [52].…”

Section: A Drl For Internet Of Quantum Dronesmentioning

confidence: 99%

Deep Reinforcement Learning for Internet of Drones Networks: Issues and Research Directions

Aboueleneen

Alwarafy

Abdallah

2023

IEEE Open J. Commun. Soc.

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Internet of Drones (IoD) is one of the promising technologies to enhance the performance of wireless networks. Deploying IoD to assist wireless networks, however, needs to address various design issues. Due to the highly dynamic nature of IoD networks, conventional methods are expected to encounter inadequacies that can be resolved using emerging deep reinforcement learning (DRL) techniques. In this paper, we discuss the application of DRL for addressing various issues in IoD networks. We first overview the main features, types, applications, and services of IoD networks. Then, we briefly discuss some DRL algorithms used to address the issues and challenges of IoD networks. After that, we explain the most crucial issues in IoD networks and discuss some papers that show how DRL can address them. Finally, we provide insights into some promising research directions in the context of using DRL in IoD networks.

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Futuristic view of the Internet of Quantum Drones: Review, challenges and research agenda

Cited by 53 publications

References 159 publications

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Role of cybersecurity and Blockchain in battlefield of things

Deep Reinforcement Learning for Internet of Drones Networks: Issues and Research Directions

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