• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.3
• /
• pp.69-75
• /
• 2018

Recently, various convergence technologies are attracting attention due to the block chain innovation technology in the M2M environment. Although the block-chain-based technology is known to be secure in its own right, there are various problems such as security and weight reduction in various M2M environments connected with this. In this paper, we propose a new lightweight method for the hash tree generation of block chains to solve the lightweight problem. It is designed considering extensibility without affecting the existing block chain. Performance analysis shows that the computation performance increases with decreasing the existing hash length.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.4C
• /
• pp.326-332
• /
• 2002

To transfer the large size of data more quickly among cluster nodes, the lightweight messaging scheme has been developed. VIA(Virtual Interface Architecture) allows that user can directly communicate with network devices without any interference of kernel and has become a communication protocol for clusters. But one must spend a lot of time to be skillful with it because of difficulties of programming. Therefore, we propose an easier library called EVIL(Easy-to-use Virtual Interface Library) that developers can easily deal with. We evaluated the performance of EVIL, Native VIA, TCP/IP respectively.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.1
• /
• pp.17-26
• /
• 2019

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol proposed by the IETF for use in IoT environments. CoAP was designed as a lightweight machine-to-machine protocol for resource constrained environments. Due to the strength of low overhead, the number of CoAP devices is expected to rise rapidly. When CoAP runs over UDP for wireless sensor networks, CoAP needs to support congestion control mechanisms. Since the default CoAP defines a minimal mechanism for congestion control, several schemes to improve the mechanism have been proposed. To keep CoAP lightweight, the majority of the schemes have been focused mainly on how to measure RTT accurately and how to set RTO adaptively according to network conditions, but other approaches such as rate-based congestion control were proposed more recently. In this paper, we survey the literature on congestion control for CoAP and discuss the future research directions.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.6 s.38
• /
• pp.239-252
• /
• 2005

Ubiquitous Sensor Network(USN) , the core technology for the Ubiquitous environments ,must be operated in the restrictive battery capacity and computing. From this cause, USN needs the lightweight design for low electric energy and the minimum computing. The previous mutual authentication. based on J$\emptyset$sang's trust model, in USN has a character that makes the lightweight mutual authentication possible in conformity with minimum computing. But, it has an imperfection at the components of representing the trust from a lightweight point of view. In this paper, we improve on the J$\emptyset$sang's trust model to apply a lightweight mutual authentication in USN. The proposed trust model in USN defines the trust information with the only degree of trust-entity(x)'s belief. The defined trust information has a superiority over the J$\emptyset$sang's trust model from a computing Point of view. because it computes information by Probability and logic operation(AND).

• Journal of Internet of Things and Convergence
• /
• v.7 no.1
• /
• pp.1-7
• /
• 2021

The IoT market continues to expand and grow, but the security threat to IoT devices is also increasing. However, it is difficult to apply the security technology applied to the existing system to IoT devices that have a problem of resource limitation. Therefore, in this paper, we present a service that can improve the security of IoT devices by presenting authentication and lightweight cryptographic algorithms that can reduce the overhead of applying security features, taking into account the nature of resource limitations of IoT devices. We want to apply these service to home network IoT equipment to provide security. The authentication and lightweight cryptographic algorithm application protocols presented in this paper have secured the safety of the service through the use of LEA encryption algorithms and secret key generation by users, IoT devices and server in the IoT environment. Although there is no difference in speed from randomly generating secret keys in experiments, we verify that the problem of resource limitation of IoT devices can be solved by additionally not applying logic for secret key sharing to IoT devices.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3904-3922
• /
• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of Convergence for Information Technology
• /
• v.12 no.2
• /
• pp.23-29
• /
• 2022

Recently, due to the increase in the use of Internet of Things (IoT) devices, the amount of data transmitted and processed to cloud computing servers has increased rapidly. As a result, network problems (delay, server overload and security threats) are emerging. In particular, edge computing with lower computational capabilities than cloud computing requires a lightweight authentication algorithm that can easily authenticate numerous IoT devices.In this paper, we proposed a key-agreement protocol of a lightweight algorithm that guarantees anonymity and forward and backward secrecy between IoT and edge devices. and the proposed algorithm is stable in MITM and replay attacks for edge device and IoT. As a result of comparing and analyzing the proposed key-agreement protocol with previous studies, it was shown that a lightweight protocol that can be efficiently used in IoT and edge devices.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.17-25
• /
• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.3
• /
• pp.151-158
• /
• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.79-92
• /
• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.