• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.58-65
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• 2014

A recent issued Internet of Things (IoT) is based on the service that everything around us is exchanging the information and reacting upon these information. These IoT services are mainly dealing with the information that was generated by the information nodes of IoT networks such as sensors, where the way how the information from information nodes should be dealt with is very important in terms of service reliability in IoT networks. This paper introduces a new scheme for service reliability and energy efficiency that is reducing the energy consumption of actuator node reacting upon the request messages from the information nodes in IoT networks.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.79-85
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• 2022

Internet of Things (IoT) connects devices with various platforms, computing power, and functions. Due to the diversity of networks and the ubiquity of IoT devices, demands for security and privacy are increasing. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, while at the same time effective enough to be implemented in devices with long-range specifications. In this paper, we present the performance and memory limitations of modern cryptographic primitives and schemes for different types of devices that can be used in IoT. In addition, detailed performance evaluation of the performance of the most commonly used encryption algorithms in low-spec devices frequently used in IoT networks is performed. To provide data protection, the binary ring uses encryption asymmetric fully homomorphic encryption and symmetric encryption AES 128-bit. As a result of the experiment, it can be seen that the IoT device had sufficient performance to implement a symmetric encryption, but the performance deteriorated in the asymmetric encryption implementation.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.13-24
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• 2021

Industrial networks has been developing various technologies from fieldbus technology to industrial Ethernet and time-sensitive networking. The industry expects that the 5G mobile network will solve the diverse and highly specific industrial site requirements. Accordingly, 3GPP has been developing standard functions to provide ultra-high reliability, ultra-high speed, ultra-connection, and ultra-low latency services, and 3GPP Rel-16 began developing ultra-low latency and ultra-high reliability communication functions for 5G mobile networks to support vertical industries. In this paper, we show the related standardization trends and requirements to apply industrial IoT service scenarios to 5G mobile networks, and in particular, we introduce 5G system features and extended 5G system architecture to provide time sensitive communication and time synchronization services.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.262-264
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• 2023

Internet of Things (IoT) networks currently employ an increased number of users and applications, raising their susceptibility to cyberattacks and data breaches, and endangering our security and privacy. Intrusion detection, which includes monitoring and analyzing incoming and outgoing traffic to detect and prohibit the hostile activity, is critical to ensure cybersecurity. Conventional intrusion detection systems (IDS) are centralized, making them susceptible to cyberattacks and other relevant privacy issues because all the data is gathered and processed inside a single entity. This research aims to create a blockchain-based architecture to support federated learning and improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.122-127
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• 2016

In this paper, new spectrum sensing schemes based on analog/RF front-end processing are introduced for IoT wireless sensor networks. While the conventional approaches for wireless channel cognition have been issued in signal processing area, the RF spectrum cognition concept makes it feasible to achieve cognitive wireless sensor networks (C-WSNs). The spectrum cognition at RF processing is categorized as four kinds of sensing mechanisms. Two recent reseaches are described as promising candidates for the C-WSN. One senses spectrum by the frequency discriminating receiver, the other senses and detects from the frequency selective super-regenerative receiver. The introduced systems with simple and low-power RF architectures play dual roles of channel sensing and demodulation. simultaneously. Therefore, introduced spectrum sensing receivers can be one of the best candidates for IoT wireless sensor devices in C-WSN environments.

• Journal of IKEEE
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• v.22 no.3
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• pp.590-598
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• 2018

Recently, interest in IoT(Internet of Things) technology, which provides Internet services to objects, is increasing. IoT offers a variety of services in home networks, healthcare, and disaster alerts. IoT with LLN(Low Power & Lossy Networks) feature frequently loses sensor node. RPL, the standard routing protocol of IoT, performs global repair when data loss occurs in a sensor node. However, frequent loss of sensor nodes due to lower sensor nodes causes network performance degradation due to frequent full path reset. In this paper, we propose an additional selection method of the storage mode sensor node to solve the network degradation problem due to the frequent path resetting problem even after selecting the storage mode sensor node, and propose a method of equalizing the total path resetting number of each instance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.5-23
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• 2012

Nowadays, most researchers and manufacturers always pay attention on wireless sensor networks (WSNs) due to its potential applications in many regions such as military, industrial and civilian areas. WSNs are the basic components of Internet of Things (IoT) and the key to machine-to-machine communications and the future Internet. Also, the security is an essential element for deploying WSNs. Recently the concept of trust-based mechanism was proposed in WSNs such as traditional cryptographic and authentication mechanisms. However, there is lack a survey on trust management for WSNs, IoT even future Internet. In this paper, we discuss the concept and potential application areas of trust management for WSNs and IoT worlds. Furthermore, we survey different trust management issues (i.e., cluster, aggregation, reputation). Finally, future research directions with respect to trust management in WSNs and future IoT world are provided. We give not only simple WSNs for IoT environments but also a simulated bootstrap platform to provide the discussion of open challenges and solutions for deploying IoT in Future Internet.

• Convergence Security Journal
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• v.14 no.7
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• pp.45-51
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• 2014

The most important point in the QoS management system to ensure the quality of the IoT system design goal is quality measurement system and the quality evaluation system. This research study is a matrix model for the IoT based on key quality measures by diagnosis system structure and function. Developing for the quality metrics measured Internet of Things environment will provide the foundation for the Internet of Things quality measurement/analysis. IoT matrix system for quality evaluation is a method to describe the functional requirements and the quality requirements in a single unified table for quality estimation performed. Comprehensive functional requirements and quality requirements by assessing the association can improve the reliability and usability evaluation. When applying the proposed method IoT quality can be improved while reducing the QoS signaling, the processing, the basis for more efficient quality assurances as a whole.

• 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%.