• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.1-7
• /
• 2018

The IoT environment provides an infinite variety of services using many different devices and networks. The development of the IoT environment is directly proportional to the level of security that can be provided. In some ways, lightweight cryptography is suitable for IoT environments, because it provides security, higher throughput, low power consumption and compactness. However, it has the limitation that it must form a new cryptosystem and be used within a limited resource range. Therefore, it is not the best solution for the IoT environment that requires diversification. Therefore, in order to overcome these disadvantages, this paper proposes a method suitable for the IoT environment, while using the existing block cipher algorithm, viz. the lightweight cipher algorithm, and keeping the existing system (viz. the sensing part and the server) almost unchanged. The proposed BCL architecture can perform encryption for various sensor devices in existing wire/wireless USNs (using) lightweight encryption. The proposed BCL architecture includes a pre/post-processing part in the existing block cipher algorithm, which allows various scattered devices to operate in a daisy chain network environment. This characteristic is optimal for the information security of distributed sensor systems and does not affect the neighboring network environment, even if hacking and cracking occur. Therefore, the BCL architecture proposed in the IoT environment can provide an optimal solution for the diversified IoT environment, because the existing block cryptographic algorithm, viz. the lightweight cryptographic algorithm, can be used.

• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.12
• /
• pp.630-640
• /
• 2007

As the range of sensor network's applicability is getting wider, it creates new application areas which is required real-time operation, such as military and detection of radioactivity. However, existing researches are focused on effective management for resources, existing sensor network operating system cannot support to real-time areas. In this paper, we propose the ${\mu}TMO$ model which is lightweight real-time distributed object model TMO. We design the real-time sensor network operation system ${\mu}TMO-NanoQ+$ which is based on ETRI's sensor network operation system Nano-Q+. We modify the Nano-Q+'s timer module to support high resolution and apply Context Switch Threshold, Power Aware scheduling techniques to realize lightweight scheduler which is based on EDF. We also implement channel based communication way ITC-Channel and periodic thread management module WTMT.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.378-390
• /
• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4420-4438
• /
• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• The Journal of Korea Robotics Society
• /
• v.12 no.4
• /
• pp.432-440
• /
• 2017

In this paper, we propose a jellyfish distribution recognition and monitoring system using a UAV (unmanned aerial vehicle). The UAV was designed to satisfy the requirements for flight in ocean environment. The target jellyfish, Aurelia aurita, is recognized through convolutional neural network and its distribution is calculated. The modified deep neural network architecture has been developed to have reliable recognition accuracy and fast operation speed. Recognition speed is about 400 times faster than GoogLeNet by using a lightweight network architecture. We also introduce the method for selecting candidates to be used as inputs to the proposed network. The recognition accuracy of the jellyfish is improved by removing the probability value of the meaningless class among the probability vectors of the evaluated input image and re-evaluating it by normalization. The jellyfish distribution is calculated based on the unit jellyfish image recognized. The distribution level is defined by using the novelty concept of the distribution map buffer.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.976-985
• /
• 2017

Wireless mesh network (WMN) is a promising technology for building a cost-effective and easily-deployed wireless networking infrastructure. To efficiently utilize limited radio resources in WMNs, packet transmissions (particularly, redundant packet transmissions) should be carefully managed. We therefore propose a lightweight traffic redundancy elimination (LTRE) scheme to reduce redundant packet transmissions in software-defined wireless mesh networks (SD-WMNs). In LTRE, the controller determines the optimal path of each packet to maximize the amount of traffic reduction. In addition, LTRE employs three novel techniques: 1) machine learning (ML)-based information request, 2) ID-based source routing, and 3) popularity-aware cache update. Simulation results show that LTRE can significantly reduce the traffic overhead by 18.34% to 48.89%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.2
• /
• pp.351-358
• /
• 2016

This paper describes an efficient hardware implementation of lightweight block cipher algorithm CLEFIA. The CLEFIA crypto-processor supports for three master key lengths of 128/192/256-bit, and it is based on the modified generalized Feistel network (GFN). To minimize hardware complexity, a unified processing unit with 8 bits data-path is designed for implementing GFN that computes intermediate keys to be used in round key scheduling, as well as carries out round transformation. The GFN block in our design is reconfigured not only for performing 4-branch GFN used for round transformation and intermediate round key generation of 128-bit, but also for performing 8-branch GFN used for intermediate round key generation of 256-bit. The CLEFIA crypto-processor designed in Verilog HDL was verified by using Virtex5 XC5VSX50T FPGA device. The estimated throughput is 81.5 ~ 60 Mbps with 112 MHz clock frequency.

• Computers and Concrete
• /
• v.12 no.2
• /
• pp.151-167
• /
• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4080-4097
• /
• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.914-934
• /
• 2016

In the mobile distributed environment, an entity may move across domains with great frequency. How to utilize the trust information in the previous domains and quickly establish trust relationships with others in the current domain remains a challenging issue. The classic trust models do not support cross-domain and the existing cross-domain trust models are not in a fully distributed way. This paper improves the outstanding Certified Reputation (CR) model and proposes a Lightweight Cross-domain Trust (LCT) model for the mobile distributed environment in a fully distributed way. The trust certifications, in which the trust ratings contain various trust aspects with different interest preference weights, are collected and provided by the trustees. Furthermore, three factors are comprehensively considered to ease the issue of collusion attacks and make the trust certifications more accurate. Finally, a cross-domain scenario is deployed and implemented, and the comprehensive experiments and analysis are conducted. The results demonstrate that our LCT model obviously outperforms the Bayesian Network (BN) model and the CR model in our cross-domain scenario, and significantly improves the successful interaction rates of the honest entities without increasing the risks of interacting with the malicious entities.