• 정보과학회 논문지
• /
• 제42권11호
• /
• pp.1423-1429
• /
• 2015

최근, 인터넷이 확장됨에 따라 새로운 가치를 생산하는 활용성이 증가되고 있다. 사물인터넷(Internet of Things)은 미래인터넷의 새로운 개념으로, 네트워크 물리적 객체들의 상호연결을 강조하여 최근 크게 주목받고 있으나, 사물인터넷상에서 서로 다른 서비스품질 요구를 만족시키기란 상당히 어렵다. 본 논문에서는, 사물인터넷 시스템의 다양한 서비스품질요구를 만족시킬 수 있는 효율적인 자원할당 방법을 제시한다. 제안된 방법은 마르코프 게임 모델에 기초하여 시스템 성능을 최대화할 수 있도록 효율적으로 사물인터넷 자원들을 할당한다. 시뮬레이션 결과, 제안된 방법은 현재의 사물인터넷 상황에서 기존의 방식에 비해 뛰어난 성능을 보여준다.

• International Journal of Computer Science & Network Security
• /
• 제22권7호
• /
• pp.1-12
• /
• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제5권5호
• /
• pp.221-230
• /
• 2016

사물인터넷(Internet of Things, IoT) 패러다임이 대두되며 센서의 개체수가 폭발적으로 증가할 것으로 예상됨에 따라 센서 네트워크 및 센서 플랫폼 기술들이 변화되고 있다. 센서 플랫폼 중 하나인 센서 레지스트리 시스템(Sensor Registry System, SRS)은 이기종 센서 네트워크 환경에서 센서 데이터의 일관성 있는 의미 해석을 위하여 센서 메타데이터를 등록하고 관리하는 시스템이다. 하지만 기존의 SRS는 IoT 환경에 적합한 데이터 구조를 지니고 있지 않다. 따라서 이 논문은 IoT 환경에서 센서 정보들을 관리하고 등록하기 위하여 센서 레지스트리 데이터 모델을 제안한다. 기존의 SRS를 개선하기 위하여 시맨틱 센서 네트워크 온톨로지(Semantic Sensor Network Ontology, SSNO)을 분석하고, 이에 기반한 메타모델을 설계한다. 또한 설계한 메타모델을 이용하여 관계형 데이터베이스의 테이블로 구축하고 SRS를 웹 애플리케이션으로 구현한다. 이 논문은 제안하는 센서 레지스트리 데이터 모델의 적합성을 검증하기 위하여 SSNO 및 센서 온톨로지 예제들을 변환하여 제안 모델에 적용한다. 평가 결과 제안 모델이 기존 연구들보다 더 풍부한 의미 표현이 가능함을 보인다.

• 한국컴퓨터정보학회논문지
• /
• 제24권1호
• /
• pp.121-128
• /
• 2019

An Internet of Things(IoT) technology which provides intelligent services by combining context-awareness based intelligences, inter-communication is made of between things and things or between things and person through the network connected with intelligent things is spreading rapidly. Especially as this technology is converged into smart device, mobile, cloud, big data technologies, it is applied into various domains. Therefore, this is different from existing Web or Mobile Application. New types of IoT applications are emerging by adapting IoT into Web or mobile. Because IoT application is not only focused on software but also considering hardware or things aspect, there are limitations existing development process. Existing development processes don't consider analysis and design techniques considering both hardware and things. We propose not only a meta-model for development process which can support IoT application's development but also meta-models for main activities in this paper. Especially we define modeling elements by using UML's extension mechanisms, provide development process, and suggest design techniques how to apply those elements into IoT application's modeling phase. Because there are many types of IoT application's type, we propose an Android and Arduino-based on IoT application as a case study. We expect that proposed technique can be applied into many of various IoT application development and design with a form of flexible and extensible as well as main functionalities or elements are more concretely described. As a result, it brings IoT application's flexibility and the effect of quality improvement.

• International Journal of Computer Science & Network Security
• /
• 제24권6호
• /
• pp.67-76
• /
• 2024

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

• Journal of Information Technology Applications and Management
• /
• 제26권5호
• /
• pp.27-39
• /
• 2019

The Internet of Things (IoT) is a new paradigm that is revolutionizing computing. It is intended that all objects around us will be connected to the network, providing "anytime, anywhere" access to information. This study introduces IoT with Kolb's learning style in order to enhance the learning experience especially for inclusive education for primary and secondary schools where delivery of knowledge is not limited to physical, cognitive disabilities, human diversity with respect to ability, language, culture, gender, age and of other forms of human differences. The article also emphasizes the role of learning style as a discovery process that incorporates the characteristics of problem solving and learning. Kolb's Learning Style was chosen as it is widely used in research and in practical information systems applications. A consistent pattern of finding emerges by using a combination of Kolb's learning style and internet of things where specific individual differences, learning approach differences and IoT application differences are taken as a main research framework. Further several suggestions were made by using this combination to IoT architecture and smart environment of internet of things. Based on these suggestions, future research directions are proposed.

• 사물인터넷융복합논문지
• /
• 제5권2호
• /
• pp.81-87
• /
• 2019

인터넷을 통한 정보화시대로 접어든지 얼마되지 않아서 급격하게 4차 산업혁명으로 변화가 일어나면서 모든 사물을 이용한 사물인터넷의 시대로 급격하게 변화가 되고 있다. 교육에서도 사물인터넷으로의 변화에 따라 4차산업혁명에 맞는 교육에 대한 관심이 높아지고 있다. 인터넷을 활용한 과제중심학습(NetPBL) 방법에서 Triz를 활용한 과제중심학습(T-PBL)으로 변화가 필요하다. 이에 본 논문에서는 Triz를 활용한 과제중심학습(T-PBL) 방법에 초점을 두고 그 활용의 필요와 중요성을 살펴보고자 한다. T-PBL을 위한 도구로 Triz를 활용한 수업 모델을 제안한다. Triz는 창의적 방법으로 문제를 해결하기 위한 도구로 이용이 되고 있다. 사물인터넷과 관련된 블록체인 시스템 보안 수업에 Triz를 적용한 모델을 설계하도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권5호
• /
• pp.2396-2413
• /
• 2018

Trust management is one of the most challenging issues for the highly heterogeneous Internet of Things (IoT). In the context of the IoT, it is difficult to evaluate the node's trustworthiness in the same trust model when a node provides different services. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of IoT environments. With these issues in mind, this paper propose a robust and adaptive trust management system for the IoT that is able to measure the trustworthiness of nodes based on feedbacks collected from participants in a specific context and ensure the availability of trust management services. The main contributions of our system are: 1) Proposing a partly decentralized trust management framework, which improves the resiliency of the trust mechanism; 2) Proposing an adaptive trust evaluation scheme and a three-dimensional context representation makes trust evaluation more accurate and specific; 3) Enhancing the adaptive trust evaluation scheme by incorporating a bad behavior factor in trust estimation, which efficiently distinguishes misleading feedbacks from On-Off attacks. Simulation results show the good performance of the proposed system and especially show effectiveness against On-Off attacks compared to other trust mechanisms.

• 한국정보통신학회논문지
• /
• 제20권7호
• /
• pp.1342-1354
• /
• 2016

정보기술인 인터넷과 하드웨어기술의 급진적인 발전에 따라 모든 사물에 인터넷을 연결하여 사물끼리 또는 사물과 사람사이에 의사소통을 하는 사물인터넷의 보급률과 이용률이 지속적으로 증가하고 있다. 이미 스마트 워치를 중심으로 한차례 사물인터넷 돌풍이 일어난 상태이며, 냉장고, 세탁기, 전구, 스위치 등 집안의 모든 사물을 스마트폰으로 조작하는 스마트 홈 킷이 현실화되고 있다. 각각의 디바이스는 자체적인 기능 구현이 가능하고, 중심 역할을 처리하는 허브를 통해 좀 더 지능화되고 협력적인 기능을 제공한다. 하지만 사물인터넷 디바이스의 양이 급증하는 것에 비해 사물인터넷 기반 소프트웨어의 품질 평가에 관한 연구는 매우 부족하여 그 품질기준이 명확하지 않다. 특히 IoT 기반 SW는 사물인터넷 디바이스를 통해 활용되기에 이동성과 휴대성, 실시간 접근성과 같은 특징과 디바이스라는 것에 대한 하드웨어적인 특징을 포괄하고 있기 때문에 포함하기에 일반적인 소프트웨어와는 차별화된 품질기준과 평가모델이 필요하다. 본 논문에서는 이러한 필요성에 따라 사물인터넷 소프트웨어 평가모델을 제안하고자 한다. 국제표준 ISO/IEC 25000의 품질 특성을 바탕으로 본 논문의 평가 모델을 제시하고, 시나리오 기반 사례연구를 수행하여 검증을 하였다.

• Journal of Information Processing Systems
• /
• 제20권2호
• /
• pp.215-225
• /
• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.