FANET Application Scenarios and Mobility Models

Bujari, Armir; Palazzi, Claudio E.; Ronzani, Daniele

doi:10.1145/3086439.3086440

Cited by 65 publications

(31 citation statements)

References 26 publications

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“…As for the drone mobility model, Bujari et al [ 25 ] defined some drone scenarios and mobility models. As an overview, the authors categorize a drone’s mobility model as pure randomized, time-dependent, path-planned, group mobility, or topology-control-based.…”

Section: Related Workmentioning

confidence: 99%

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Valentino

Jung

2018

Sensors

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Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

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Section: Related Workmentioning

confidence: 99%

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Valentino

Jung

2018

Sensors

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“…Specifically for the UAS reconnaissance, a pheromone-guided mobility model has been suggested in [ 31 ]. For different FANET application scenarios, various mobility models have been proposed in [ 32 ]. Despite these results, the existing approaches still fail to simulate complex operator interactions, effects of the environment, or multi-vehicle team coordination.…”

Section: Related Workmentioning

confidence: 99%

Communication Architecture in Mixed-Reality Simulations of Unmanned Systems

Martin

Faigl

Rollo

2018

Sensors

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Verification of the correct functionality of multi-vehicle systems in high-fidelity scenarios is required before any deployment of such a complex system, e.g., in missions of remote sensing or in mobile sensor networks. Mixed-reality simulations where both virtual and physical entities can coexist and interact have been shown to be beneficial for development, testing, and verification of such systems. This paper deals with the problems of designing a certain communication subsystem for such highly desirable realistic simulations. Requirements of this communication subsystem, including proper addressing, transparent routing, visibility modeling, or message management, are specified prior to designing an appropriate solution. Then, a suitable architecture of this communication subsystem is proposed together with solutions to the challenges that arise when simultaneous virtual and physical message transmissions occur. The proposed architecture can be utilized as a high-fidelity network simulator for vehicular systems with implicit mobility models that are given by real trajectories of the vehicles. The architecture has been utilized within multiple projects dealing with the development and practical deployment of multi-UAV systems, which support the architecture’s viability and advantages. The provided experimental results show the achieved similarity of the communication characteristics of the fully deployed hardware setup to the setup utilizing the proposed mixed-reality architecture.

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“…Due to various advantages and wide range of applications areas FANETs are getting attentions of research community around the globe. Three main applications are described below [3]:…”

Section: Introductionmentioning

confidence: 99%

Throughput optimization for flying ad hoc network based on position control using genetic algorithm

Liu

Huo

Wang

2020

Int. J. Metrol. Qual. Eng.

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In some complex applications, Flying Ad Hoc Network (FANET) can provide important support for multi UAV (Unmanned Aerial Vehicle) cooperation. In FANET each UAV is equivalent to a router, and the wireless link between them forms a network to achieve the purpose of relay communication. Throughput is an important network performance, and the position of UAV nodes affects it. In this paper, we analyze the influencing factors of FANET throughput with UAV position and terminator selection in first; Secondly we construct the mathematical model of throughput optimization of FANET; Thirdly we propose an algorithm based on genetic algorithm to optimize the position of UAV, and then maximize the throughput. Preparing for using genetic algorithm, we design the related details: Numbering Area, Determining the adjacency matrix and correlation matrix, determining the range of UAV node position movement. The key points of the genetic algorithm for FANET is proposed include the following aspects: coding and population initialization, fitness function, and chromosome replication/crossover/mutation and termination criteria. At last, Matlab is used to simulate the proposed algorithm from three aspects: performance, effect of Radius of Position Constraint (RPC) and effect of Radius of Particle Size (RPS). The results show that the throughput can reach the expected goal by controlling the UAV　position, and the optimization speed is related to the RPC and RPS.

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FANET Application Scenarios and Mobility Models

Cited by 65 publications

References 26 publications

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Communication Architecture in Mixed-Reality Simulations of Unmanned Systems

Throughput optimization for flying ad hoc network based on position control using genetic algorithm

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