• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.125-128
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• 2011

This paper describes an efficient hardware implementation of HIGHT block cipher algorithm, which was approved as standard of cryptographic algorithm by KATS(Korean Agency for Technology and Standards) and ISO/IEC. The HIGHT algorithm, which is suitable for ubiquitous computing devices such as a sensor in USN or a RFID tag, encrypts a 64-bit data block with a 128-bit cipher key to make a 64-bit cipher text, and vice versa. For area-efficient and low-power implementation, we optimize round transform block and key scheduler to share hardware resources for encryption and decryption. The HIGHT64 core synthesized using a $0.35-{\mu}m$ CMOS cell library consists of 3,226 gates, and the estimated throughput is 150-Mbps with 80-MHz@2.5-V clock.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2769-2774
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• 2013

The key of USN(Ubiquitous Sensor Network) technology is low power wireless communication technology and proper resource allocation technology for efficient routing. The distinguished resource allocation method is needed for efficient routing in sensor network. To solve this problems, we propose an algorithm that can be adopted in USN with making up for weak points of PQ and WRR in this paper. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class in sensor network. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.