• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.17-26
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• 2018

Recently, through the emergence and convergence of Internet services, the unified Internet of thing(IoT) service platform have been researched. Currently, the IoT service is constructed as an independent system according to the purpose of the service provider, so information exchange and module reuse are impossible among similar services. In this paper, we propose a operation based service classification algorithm for various services in order to provide an environment of unfied Internet platform. In implementation, we classify and cluster more than 100 commercial IoT services. Based on this, we evaluated the performance of the proposed algorithm compared with the K-means algorithm. In order to prevent a single clustering due to the lack of sample groups, we re-cluster them using K-means algorithm. In future study, we will expand existing service sample groups and use the currently implemented classification system on Apache Spark for faster and more massive data processing.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.107-123
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• 2015

Internet of Things (IoT) is being globally spotlighted as a fundamental technology to realize hyper-connected society, and as new growth engines of nation and enterprises. Although the technical aspect of IoT receives a great deal of attention as a new business opportunity, the business aspect of IoT is suffering insufficient scholarly research and objective insight. Thus, the business aspect of IoT requires a thorough research on its service market and business opportunities. In order to stimulate the IoT service market and facilitate objective statistic data aggregation, this paper aims to suggest an IoT service categorization model. This model is comprised of three perspectives which are IoT purpose, IoT Player, and IoT Domain; they function as tools to comprehensively analyze the IoT industry. Efficacy of this model has been confirmed by simulating 117 IoT services on the model, in which the results successfully offered the market trend of the IoT service based on Case. This study is able to apply for basic research of IoT-Service Development Plan, and provide practical implications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.613-614
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• 2017

Recently, research on the automation of home IoT has been carried out in which IoT (Internet of Things) is applied inside the home. However, the conventional IoT automation system has a problem that the operation of the device is limited only by the threshold value of the sensor, so that the device may collide and interfere with each other and the efficiency of the Task is low due to the malfunction of the device. In this paper, we propose a Situation-specific task classification algorithm to solve these problems. Using the sensor threshold and the current date as classification values in the decision tree, the task according to the internal situation of the home is classified and the corresponding device is selected and proceeded. Therefore, it is expected that the users will be provided with a service that changes flexibly according to changes in the internal situation of the home, and the accuracy of the operation will be increased by reducing the malfunction of the device and the collision between the devices.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.95-101
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• 2019

Sensor application systems for remote monitoring and control are required, such as the establishment of databases and IoT service servers, to process data being transmitted and received through radio communication modules, controllers and gateways. This paper designs and implements database server, IoT service server, data processing middleware and IoT management system for IoT based services based on the controllers, communication modules and gateway middleware platform developed. For this, we firstly define the specification of the data packet and control code for the information classification of the sensor application system, and also design and implement the database as a separate server for data protection and efficient management. In addition, we design and implement the IoT management system so that functions such as status information verification, control and modification of operating environment information of remote sensor application systems are carried out. The implemented system can lead to efficient operation and reduced management costs of sensor application systems through site status analysis, setting operational information, and remote control and management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.742-743
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• 2017

The smart factory market is expected to show high growth rate in the future, supported by demand for manufacturing innovation in order to overcome structural low growth. Especially in the future manufacturing industry, robots are combined with IT, becoming the most important core technology. In this paper, we proposed and implemented state information classification method for IoT-based robot control in smart manufacturing environment.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.119-128
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• 2013

As ICT technology becomes advanced, the importance of future internet is emphasized and in part of that, M2M (Machine-to Machine communications) is magnified in terms of importance and usage in public and private sector. M2M is emerging as a next generation strategic industry but there is no existing analyzed data or market classification, so it disrupts establishing policies on the M2M industry. As the technology is progressing, the evolution from M2M to IoT (Internet of Things) has started and many countries actively try to find technological trend through market analysis in order to develop new growth engine. Therefore, in order to strengthen competitiveness, we should secure differentiated capabilities in industry and service. This article examines Korea's domestic market and international market trends in IoT and analyses the economic impact of the IoT industry using quantitative methodology and evaluates relations between the IoT industry and other relevant industries. As a result, the effect of IoT industry on production inducement is KRW474.6 billion; the effect on value-added inducement is KRW314.7 billion; and it is measured that 3,628 jobs will be created by the IoT industry.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1138-1140
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• 2017

센서 및 초근거리 통신 기술의 발전으로 다양한 사물인터넷 서비스가 등장하였다. 현재 사물인터넷 서비스는 단일화된 서비스만을 제공하고 있지만 서비스들이 융합된 새로운 서비스로 발전되고 있다. 서비스 융합시 발생할 수 있는 프로토콜의 다양성, 모듈의 중복성등의 문제를 해결하기 위하여 통합 서비스 플랫폼의 필요성이 대두되었다. 이에 본 연구에서는 보다 효율적인 통합 서비스 플랫폼을 제공하기 위한 기반 연구로 사물인터넷 서비스 분류 알고리즘을 제안한다. 제안하는 서비스 분류 알고리즘은 서비스 별 세부 동작을 기반으로 구성된다. 그리고 후속 연구로 실제 서비스에 제안한 서비스 분류알고리즘을 적용하여 서비스간 유사도 분석을 통한 서비스 그룹화에 관한 연구를 진행할 예정이다.