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Reinforcement Learning-Based Trajectory Optimization of Solar Panel-Equipped UAV BS for Energy Efficiency
Dong Uk Kim, Choong Seon Hong, Seong Bae Park, Jong Won Choi
http://doi.org/10.5626/JOK.2023.50.10.899
5G and B5G wireless communication systems use new bands, such as millimeter-wave, to meet user requirements. However, these new bands have limitations such as lower diffraction, lower transmittance, and stronger straightness than traditional frequency bands. To address these limitations, a cellular communication paradigm supported by Unmanned Aerial Vehicle (UAV), makes communication services more flexible than existing ground base stations. However, UAVs have limited battery capacity, which affects the life of telecommunications services. To address this problem, this paper considers UAVs equipped with solar panels. Movement toward energy generation and altitude for user data rate maximization due to solar power of UAVs can consume a lot of energy. Energy generation, data rate maximization, and energy consumption have a trade-off relationship. Therefore, in this study, we proposed a system to locate UAVs that could optimize the above trade-off relationship using agents learned using a reinforcement learning algorithm called "Proximal Policy Optimization (PPO)" and compare the system proposed in this paper.
A Study on Service-based Secure Anonymization for Data Utility Enhancement
Chikwang Hwang, Jongwon Choe, Choong Seon Hong
Personal information includes information about a living human individual. It is the information identifiable through name, resident registration number, and image, etc. Personal information which is collected by institutions can be wrongfully used, because it contains confidential information of an information object. In order to prevent this, a method is used to remove personal identification elements before distributing and sharing the data. However, even when the identifier such as the name and the resident registration number is removed or changed, personal information can be exposed in the case of a linking attack. This paper proposes a new anonymization technique to enhance data utility. To achieve this, attributes that are utilized in service tend to anonymize at a low level. In addition, the anonymization technique of the proposal can provide two or more anonymized data tables from one original data table without concern about a linking attack. We also verify our proposal by using the cooperative game theory.
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