• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.160-164
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• 2017

Public key cryptography (PKC) is the basic building block for the cryptography applications such as encryption, key distribution, and digital signature scheme. Among many PKC, elliptic curve cryptography (ECC) is the most widely used in IT systems. Recently, very efficient Montgomery-Twisted-Edward (MoTE)-ECC was suggested, which supports low complexity for the finite field arithmetic, group operation, and scalar multiplication. However, we cannot directly adopt the MoTE-ECC to new PKC systems since the cryptography is not fully evaluated in terms of performance on the Internet of Things (IoT) platforms, which only supports very limited computation power, energy, and storage. In this paper, we fully evaluate the MoTE-ECC implementations on the representative IoT devices (16-bit MSP processors). The implementation is highly optimized for the target platform and compared in three different factors (ROM, RAM, and execution time). The work provides good reference results for a gradual transition from legacy ECC to MoTE-ECC on emerging IoT platforms.

• Annual Conference of KIPS
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.473-474
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• 2014

Radio frequency identification system (RFID) is an automatic technology and aids machines or computers to identify objects, record metadata or control individual target through wireless waves. Connecting RFID reader to the terminal of Internet, the readers can identify, track and monitor the objects attached with tags globally, automatically, and in real time, if needed. This is the so-called Internet of Things (IOT). RFID is often seen as a prerequisite for the IOT. This paper surveys the technologies of RFID and IOT, discusses the applications and challenges of RFID technology used in IoT.

• Journal of Information Technology Applications and Management
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• v.26 no.2
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• pp.61-73
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• 2019

In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.

• Electronics and Telecommunications Trends
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• v.37 no.5
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• pp.44-53
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• 2022

The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.

• Annual Conference of KIPS
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• 2020.11a
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• pp.818-821
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• 2020

The artificial intelligence software market is developing in various fields world widely. In particular, there is a wide variety of applications for image recognition technology using deep learning. This study intends to apply image recognition technology to the 'Home Gardening' market growing rapidly due to COVID-19, and aims to build a small-scale smart farm in the house using artificial intelligence and IoT technology for convenient crop cultivation for busy people living in cities. This intelligent farm system includes an automatic image recognition function and recommendation function based on temperature and humidity sensor-based indoor environment analysis.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.97-102
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• 2020

Multi-Channel Switch ICs for IoT have integrated several solid state structure low ON-resistance bi-directional relay MOS switches with level shifter to drive high voltage and they should be independently controlled by external serialized logic control. These devices are designed for using in applications requiring high-voltage switching control by low-voltage control signals, such as medical ultra-sound imaging, ink-jet printer control, bare board open/short and leakage test system using Kelvin 4-terminal measurement method. This paper describes implementation of analog switch control block and its verification using Field programmable Gate Array (FPGA) test pattern generation. Each block has been implemented using Verilog hardware description language then simulated by Modelsim and prototyped in a FPGA board. Compare to conventional IC, The proposed architecture can be applied to fields where multiple entities need to be controlled simultaneously in the IoT environment and the proposed pattern generation method can be applied to test similar types of ICs.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.3
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• pp.123-131
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• 2015

With a prospective of rapid deployment of IOT, a systematic approach to derive a business strategy for various possible scenarios of IOT applications is in great demand. In this paper, a conceptual framework that can be utilized for the purpose of assessing the market potential and of setting up an initial business strategy for IOT deployment is suggested. The framework consists of utilization of well-known value curve analysis, ecosystem analysis and house of quality tools. The value curve analysis is utilized to identify value-enhancing components of consumers as well as relative strengths of suppliers. The ecosystem analysis is used to identify relevant players of the supply chain and their mutual relationships. The house of quality is suitable for developing the initial business strategy of the supplier by converting consumer requirements identified by value curve analysis into technical requirements for the supplier. In this paper, we applied our proposed framework to two services that have high potentiality of being benefited by IOT: car-sharing service and telehealth service.