• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.1-7
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• 2020

This paper proposes a link adaptation method for Underwater Internet of Things (IoT), which reduces power consumption of sensor nodes and improves the throughput of network in underwater IoT network. Adaptive Modulation and Coding (AMC) technique is one of link adaptation methods. AMC uses the strong correlation between Signal Noise Rate (SNR) and Bit Error Rate (BER), but it is difficult to apply in underwater IoT as it is. Therefore, we propose the machine learning based AMC technique for underwater environments. The proposed Modulation Coding and Scheme (MCS) prediction model predicts transmission method to achieve target BER value in underwater channel environment. It is realistically difficult to apply the predicted transmission method in real underwater communication in reality. Thus, this paper uses the high accuracy BER prediction model to measure the performance of MCS prediction model. Consequently, the proposed AMC technique confirmed the applicability of machine learning by increase the probability of communication success.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.41-48
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• 2020

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.351-354
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• 2015

지구 전체 표면적의 약 70%인 바다는 석유를 포함한 각종 수산자원이 풍부하지만 인간은 바다로 접근하기 위해 파도, 태풍 등의 날씨에 절대적인 영향을 받기 때문에 쉽게 접근하기 어렵다. 이 경우 해양 관련 정보를 얻고 분석 및 활용하기 위해 IoT (Internet of Things)의 기반 기술인 센서네트워크를 사용할 수 있다. 하지만 바다에 센서네트워크를 적용하기 위해서는 파도, 태풍을 포함한 염분 등을 충분히 고려해야 한다. 게다가 수중 통신을 사용할 경우 수중에서는 전파를 사용할 수 없기 때문에 음파와 같이 수중에서 통신이 가능한 방법을 선택해야 한다. 따라서 본 논문에서는 해양센서네트워크 기술의 현장 적용을 위한 고려사항에 대해 논의하고, 실제 가두리 양식장에 설치 운용한 사례를 소개한다.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.33-40
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• 2020

This work tackles the problem of conventional reinforcement learning scheme which has a relatively long training time to reduce energy consumption in underwater network. The enhanced scheme adjusts the learning range of reinforcement learning based on distance estimation. It can be reduce the scope of learning. To take account the fact that the distance estimation may not be accurate due to the underwater wireless network characteristics. this research added noise in consideration of the underwater environment. In simulation result, the proposed AQ-NAV scheme has completed learning much faster than existing method. AQ-NAV can finish the training process within less than 40 episodes. But the existing method requires more than 120 episodes. The result show that learning is possible with fewer attempts than the previous one. If AQ-NAV will be applied in Underwater Networks, It will affect energy efficiency. and It will be expected to relieved existing problem and increase network efficiency.