Enabling drone as a service: OneM2M-based UAV/drone management system

Choi, Sung-Chan; Sung, Nak-Myung; Park, Jong-Hong; Ahn, Il-Yeop; Kim, Jae-Ho

doi:10.1109/icufn.2017.7993739

Cited by 21 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• UAV control and management: When the number of UAVs is high, their control and management from a remote Internet locations can become complex due the frequent data transmissions between the concurrent UAVs and the IoT ground devices. Although some IoFT works have addressed this issue [170,171], efficient algorithms should be proposed to provide some UAV control and management functionalities, such as subscription and notification, data management, UAV localization, group management, etc. [148], [149], [150], [151], [152] Several IoFT parameters must be optimized, such as:…”

Section: Ioft Challenges and Future Perspectivesmentioning

confidence: 99%

See 1 more Smart Citation

Internet of Flying Things (IoFT): A Survey

Zaidi

Atiquzzaman

Calafate

2021

Computer Communications

View full text Add to dashboard Cite

Section: Ioft Challenges and Future Perspectivesmentioning

confidence: 99%

“…• Physical layer. UAV control and management [170], [171] Proposed efficient algorithms for UAV control and management which provide some functionalities, such as:…”

Section: Ioft Challenges and Future Perspectivesmentioning

confidence: 99%

Internet of Flying Things (IoFT): A Survey

Zaidi

Atiquzzaman

Calafate

2021

Computer Communications

View full text Add to dashboard Cite

“…Choi et al [26] used a standards-based communication scheme that is useful in realizing efficient management and control of UAV-based operations. Considering this issue, they used a global standard called one Machine-to-Machine (M2M), which provides M2M communication and a service layer to interoperate M2M and IoT solutions.…”

Section: Cloud Drone Sharing Servicesmentioning

confidence: 99%

Flight Planning Optimization of Multiple UAVs for Internet of Things

Rodrigues

Riker

Ribeiro

et al. 2021

Sensors

View full text Add to dashboard Cite

This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.

show abstract

“…Orientation tracking plays an integral part in the control and stable maintenance of unmanned aerial vehicles (UAV), called drones [ 1 , 2 ]. Nowadays, 6DoF drones have been designed for popular use due to low costs and potential controlling systems.…”

Section: Introductionmentioning

confidence: 99%

Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking

Hoang

Carratù

Paciello

et al. 2023

Sensors

View full text Add to dashboard Cite

The paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°.

show abstract

Enabling drone as a service: OneM2M-based UAV/drone management system

Cited by 21 publications

References 4 publications

Internet of Flying Things (IoFT): A Survey

Internet of Flying Things (IoFT): A Survey

Flight Planning Optimization of Multiple UAVs for Internet of Things

Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking

Contact Info

Product

Resources

About