• Journal of Digital Contents Society
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• v.16 no.1
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• pp.165-172
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• 2015

This research has been conducted to know the effect of advertisement according to consumer's need for cognition in the advertisement which is realized technique of the Internet of Things(IoT). The researchers distributed questionnaires and video advertisement, which is realized by using IoT technologies, to university students in the capital area after simple orientation about IoT advertisement. The result was that people who has high consumer's need for cognition showed more positive attitude to advertisement and it was statistically meaningful. As a result, the research could draw a conclusion that people have different attitude to advertisement that is realized using IoT technologies based on the consumer's need for cognition.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.77-84
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• 2024

Over the past few years, IoT edges have begun to emerge based on new low-latency communication protocols such as 5G. However, IoT edges, despite their enormous advantages, pose new complementary threats, requiring new security solutions to address them. In this paper, we propose a cloud environment-based IoT edge architecture model that complements IoT systems. The proposed model acts on machine learning to prevent security threats in advance with network traffic data extracted from IoT edge devices. In addition, the proposed model ensures load and security in the access network (edge) by allocating some of the security data at the local node. The proposed model further reduces the load on the access network (edge) and secures the vulnerable part by allocating some functions of data processing and management to the local node among IoT edge environments. The proposed model virtualizes various IoT functions as a name service, and deploys hardware functions and sufficient computational resources to local nodes as needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.34-52
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• 2019

This paper proposes an intelligent system performing an application with assistance of an Internet of Things (IoT) platform to control a traffic lights system. In our proposed systems, the traffic lights can be remotely controlled through the Internet. Based on IoT platform, the traffic conditions at different intersections of roads are collected and the traffic lights are controlled in a central manner. For the software part, the algorithm is designed based on the green wave theory to maximize the green bandwidth of arterial roads while addressing a challenging issue: the rapid changes of parameters including cycle time, splits, offset, non-fixed vehicles' velocities and traffic flow along arterial roads. The issue typically happens at some areas where the transportation system is not well organized like in Vietnam. For the hardware part, PLC S7-1200 are placed at the intersections for two purposes: to control traffic lights and to collect the parameters and transmit to a host machine at the operation center. For the communication part, the TCP/IP protocol can be done using a Profinet port embedded in the PLC. Some graphical user interface captures are also presented to illustrate the operation of our proposed system.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.109-112
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• 2020

Many devices are connected on the internet to give functionalities for interconnected services. In 2020', The number of devices connected to the internet will be reached 5.8 billion. Moreover, many connected service provider such as Google and Amazon, suggests edge computing and mesh networks to cope with this situation which the many devices completely connected on their networks. This paper introduces the current state of the introduction of the wireless mesh network and edge cloud in order to efficiently manage a large number of nodes in the exploding Internet of Things (IoT) network and introduces the existing Network Time Protocol (NTP). On the basis of this, we propose a relatively accurate time synchronization method, especially in heterogeneous mesh networks. Using this NTP, multiple time coordinators can be placed in a mesh network to find the delay error using the average delay time and the delay time of the time coordinator. Therefore, accurate time can be synchronized when implementing IoT, remote metering, and real-time media streaming using IoT mesh network.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.174-178
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• 2024

IoT is collection of different "things" which are associated with open web. As all the things are connected to the Internet, it offers convenience to end users for accessing the resources from "Any Where, Any Time" throughout the globe. At the same time, open nature of IoT provides a fertile ground to an intruder for launching different security related threats. If we can no apply proper security safeguards to the IoT System, then it will be not useful to society. Authentication, Encryption, Trust Management and Secure Routing are different domains to offer security in IoT system. Among them, Authentication is very much important security service as it validates device identity before granting access to system services/ resources. Existing IoT Authentication algorithms are fail to verify device identity in unambiguous way. They are vulnerable to different security threats such as Key Stolen threat, MITM threat and Location Spoofing threat. So, it is a demand of time to design an efficient and secure Multi-factor IoT algorithm which can offer better security and validate device identity in unambiguous way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.598-599
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• 2016

Today, embedded, mobile, and cyber-physical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as "Industry 4.0" and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. We survey an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems in this paper.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.173-174
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• 2020

본 논문에서는 IoT(Internet of Things)와 릴레이(Relay)를 활용한 어항관리 시스템을 개발하였다. 기존의 어항관리시스템(Fishbowl Management System)은 가격이 높거나, 호환성이 떨어지기 때문에 개인이 도입하기에는 어려운 점이 있었다. 본 논문에서 제시하는 어항관리 시스템은 이러한 기존 시스템들의 단점을 극복하고 더 효율적인 어항 운영을 위한 대안으로서 제시된다. 가격이 낮은 미니컴퓨터와 웹 서버, 그리고 릴레이를 이용하여 장치를 제어함으로써 호환성 부족의 문제를 개선하여 어항에 사용되는 장치의 제약을 줄였다. 또한 웹 서버와 외부 데이터베이스 서버를 두어 어항의 상태를 관리, 기록할 수 있어서 어항 운영의 안정성을 높였다.

• Annual Conference of KIPS
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• 2018.10a
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• pp.939-942
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• 2018

최근 LED 조명을 활용한 가시광 통신(VLC: Visible Light Communications)기술이 주목을 받고 있다. 가시광 통신 기술은 Bluetooth, ZigBee 등과 함께 새로운 인터넷 접속 기술로 활용될 수 있으나 전송 용량의 한계 때문에 실제 적용이 힘든 상황이다. 본 논문에서는 이 문제를 해결하기 위해 키-값 데이터베이스 패러다임을 적용한 가시광 통신 기반의 사물인터넷(IoT: Internet of Things) 모델을 제시한다. 제안하는 IoT 환경은 플렛폼 서버, 게이트웨이, VLC 송신기 및 VLC 수신기로 구성된다. IoT 통신을 위한 각 장비의 기능 및 프로토콜 스텍을 설계하고 실험 환경에서 각 장치의 초기화 절차 및 데이터 전송 과정을 검증하였다. 실험결과, 각 장비의 초기 구동 시간을 포함하여 1분 이내에 VLC 기반의 IoT 환경 구축이 가능함을 확인하였다.