Prediction of information propagation in a drone network by using machine learning

Jinsoo Park; Yoojoong Kim; Junhee Seok

doi:10.1109/ICTC.2016.7763456

Prediction of information propagation in a drone network by using machine learning

Jinsoo Park, Yoojoong Kim, Junhee Seok

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

16 Citations (Scopus)

Abstract

Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones.

Original language	English
Title of host publication	2016 International Conference on Information and Communication Technology Convergence, ICTC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	147-149
Number of pages	3
ISBN (Electronic)	9781509013258
DOIs	https://doi.org/10.1109/ICTC.2016.7763456
Publication status	Published - 2016 Nov 30
Event	2016 International Conference on Information and Communication Technology Convergence, ICTC 2016 - Jeju Island, Korea, Republic of Duration: 2016 Oct 19 → 2016 Oct 21

Other

Other	2016 International Conference on Information and Communication Technology Convergence, ICTC 2016
Country/Territory	Korea, Republic of
City	Jeju Island
Period	16/10/19 → 16/10/21

Keywords

Communication
Drone
linear regression
Monte-Carlo method
Network
Supported Vector Machine

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Signal Processing

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10.1109/ICTC.2016.7763456

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Prediction of information propagation in a drone network by using machine learning. / Park, Jinsoo; Kim, Yoojoong; Seok, Junhee.
2016 International Conference on Information and Communication Technology Convergence, ICTC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 147-149 7763456.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Park, J, Kim, Y & Seok, J 2016, Prediction of information propagation in a drone network by using machine learning. in 2016 International Conference on Information and Communication Technology Convergence, ICTC 2016., 7763456, Institute of Electrical and Electronics Engineers Inc., pp. 147-149, 2016 International Conference on Information and Communication Technology Convergence, ICTC 2016, Jeju Island, Korea, Republic of, 16/10/19. https://doi.org/10.1109/ICTC.2016.7763456

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abstract = "Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones.",

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N2 - Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones.

AB - Drones cooperate with each other by transmitting and receiving packets. Therefore, it is important to conjecture the packet transmission rates within the network. However, the conventional methods are not suitable to describe the transmission patterns with satisfactory computing speed and accuracy. In this paper, we demonstrated that machine learning can successfully predict the transmission patterns in drone network. The packet transmission rates of a communication network with twenty drones were simulated, of which results were used to train the linear regression and Support Vector Machine with Quadratic Kernel (SVM-QK). We found out SVM-QK can precisely predict the communication between drones.

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KW - Monte-Carlo method

KW - Network

KW - Supported Vector Machine

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Prediction of information propagation in a drone network by using machine learning

Abstract

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Keywords

ASJC Scopus subject areas

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