SEDIBLOFRA: A Blockchain-Based, Secure Framework for Remote Data Transfer in Unmanned Aerial Vehicles

This paper investigates the process of multi-stream data transmission from several unmanned aerial vehicles (UAV) to a ground station. We can observe a mathematical model of the data transfer process at the application level of the OSI model (from flying nodes to a ground station). The Poisson – Pareto packet process is used to describe the multi-stream data traffic. The results of simulation are obtained using the network simulator NS-3. It is considered a system for emulating the process of multi-stream data transmission from UAV to a ground station. Acording to the results of studies for multi-stream data transmission it is clear that the increase of the UAV source nodes which simultaneously transmit data to a ground station needs higher requirements for Goodput.

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Large-aperture multiple quantum well modulating retroreflector for free-space optical data transfer on unmanned aerial vehicles

Optical Engineering ◽

10.1117/1.1383783 ◽

2001 ◽

Vol 40 (7) ◽

pp. 1348 ◽

Cited By ~ 76

Author(s):

G. C. Gilbreath

Keyword(s):

Quantum Well ◽

Unmanned Aerial Vehicles ◽

Free Space ◽

Data Transfer ◽

Multiple Quantum Well ◽

Optical Data ◽

Multiple Quantum ◽

Free Space Optical ◽

Large Aperture ◽

Aerial Vehicles

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Data Transfer via UAV Swarm Behaviours

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/ajtde.v6n2.142 ◽

2018 ◽

Vol 6 (2) ◽

pp. 35-57

Author(s):

Phillip Smith ◽

Robert Hunjet ◽

Aldeida Aleti ◽

Jan Carlo Barca

Keyword(s):

Machine Learning ◽

Unmanned Aerial Vehicles ◽

Data Transfer ◽

Evolution Process ◽

Heuristic Rule ◽

Upper Limit ◽

Aerial Vehicles ◽

Uav Swarm ◽

Robotic Swarm ◽

The Given

This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task. The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.

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Data Transfer via UAV Swarm Behaviours

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v6n2.142 ◽

2018 ◽

Vol 6 (2) ◽

pp. 35-57

Author(s):

Phillip Smith ◽

Robert Hunjet ◽

Aldeida Aleti ◽

Jan Carlo Barca

Keyword(s):

Machine Learning ◽

Unmanned Aerial Vehicles ◽

Data Transfer ◽

Evolution Process ◽

Heuristic Rule ◽

Upper Limit ◽

Aerial Vehicles ◽

Uav Swarm ◽

Robotic Swarm ◽

The Given

This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task. The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.

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Development of thickened semi-synthetic engine oil M-5z / 20 AERO for four-stroke gasoline engines aircraft piston of unmanned aerial vehicles (UAVs)

World of OIL Products the Oil Companies Bulletin ◽

10.32758/2071-5951-2020-0-6-44-47 ◽

2020 ◽

Vol 06 ◽

pp. 44-47

Author(s):

A.A. Moykin ◽

◽

A.S. Medzhibovsky ◽

S.A. Kriushin ◽

M.V. Seleznev ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Engine Oil ◽

Motor Oil ◽

Gasoline Engines ◽

State Research Institute ◽

Technical Requirements ◽

Aerial Vehicles ◽

State Research ◽

Industrial Batch ◽

The Russian Federation

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

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