Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Horváth, Denis; Gazda, Juraj; Šlapak, Eugen; Maksymyuk, Taras

doi:10.3390/e21111077

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Task scheduling and flow table information are maintained and updated using smart contracts. Article [54] studies application-specific functionality of drones for hybrid cellular network deployment. Studies show that prolonged use of UAVs, particularly for drones as a service (DAS), has been in limited deployment due to factors such as limited flight time, collision avoidance, and onboard hardware capacity.…”

Section: Network Structure and Characteristicsmentioning

confidence: 99%

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Phadke

Medrano

2022

Drones

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UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost and labor-intensive tasks, such as land surveying, in a faster and cheaper manner. Researchers studying UAV applications have realized that a swarm of UAVs working collaboratively on tasks can achieve better results. The dynamic work environment of UAVs makes controlling the vehicles a challenge. This is magnified by using multiple agents in a swarm. Resiliency is a broad concept that effectively defines how well a system handles disruptions in its normal functioning. The task of building resilient swarms has been attempted by researchers for the past decade. However, research on current trends shows gaps in swarm designs that make evaluating the resiliency of such swarms less than ideal. The authors believe that a complete well-defined system built from the ground up is the solution. This survey evaluates existing literature on resilient multi-UAV systems and lays down the groundwork for how best to develop a truly resilient system.

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Section: Network Structure and Characteristicsmentioning

confidence: 99%

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Phadke

Medrano

2022

Drones

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“…Drone technologies for intelligently coordinating various behaviors across diverse situations [ 1 , 2 , 3 , 4 ] become more advanced every day, which enhances their ability to perform complicated and sensitive missions, especially as the drones increase in size [ 5 , 6 , 7 ]. Escalating demands require higher levels of drone autonomy in which individual drones can make their own decisions more freely and collaboratively [ 3 ].…”

Section: Introductionmentioning

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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Evolutionary Coverage Optimization for a Self-Organizing UAV-Based Wireless Communication System

et al. 2021

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Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Cited by 3 publications

References 40 publications

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms

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

Evolutionary Coverage Optimization for a Self-Organizing UAV-Based Wireless Communication System

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