• 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.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.320-349
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• 2018

Purpose: The application of IoT is finding continuous acceptance in our daily lives, particularly, smart speakers are making life easier and convenient for consumers. This research aims to develop and test an integrated model of factors influencing consumer's adoption of voice-enabled IoT devices. Design/methodology/approach: Based on the VAM, an integrated voice-enabled IoT device adoption model is proposed. Gender differences on five constructs relating with perceived value (perceived usefulness, perceived enjoyment, perceived security risk, perceived technicality and perceived cost) was also examined through PLS-MGA technique. The usage experience of consumers was also controlled in the integrated VAM. Findings: Result shows that Perceived-Usefulness, Perceived-Enjoyment and Perceived-Cost have a strong effect on Perceived-Value. However, Perceived-Technicality and Perceived-Security-Risk are non-influential and have no significant effect on PV. Additionally, Perceived-Value and Social-Influence plays a significant role in predicting adoption intention. Gender differences also exist in consumers perception of usefulness, enjoyment and cost. In comparison to the basic value-based adoption model, the integrated model provides more insight on consumers adoption of voice-enabled IoT devices. Originality/value: Using an integrated model, this study is one of the first scholarly attempt at modelling the influential factors for adopting smart speakers i.e., voice-enabled IoT devices, with implications for improved adoption.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.59-66
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• 2018

Android Things is an Android-based platform running in Google's IoT environment. Android smartphones require permissions from application users to use certain features, but in the case of Android Things, there is no display to send request notifications to users. Therefore Does not make a request to use the permissions and automatically accepts the permissions from the system. If the privilege is used indiscriminately, malicious behavior such as system failure or leakage of personal information can be performed by a function which is not related to the function originally. Therefore, By monitoring the privileges that a device uses in an Android-based IoT system, users can proactively respond to security threats that can arise through unauthorized use of the IoT system. This paper proposes a system that manages the rights currently being used by IoT devices in the Android Things based IoT environment, so that Android-based IoT devices can cope with irrelevant use of rights.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1928-1939
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• 2017

Fog computing is another paradigm of the cloud computing, which extends the ubiquitous services to applications on many connected devices in the IoT (Internet of Things). In general, if we access a lot of IoT devices with existing cloud, we waste a huge amount of bandwidth and work efficiency becomes low. So we apply the paradigm called fog between IoT devices and cloud. The network architecture based on cloud and fog computing discloses the security and privacy issues according to mixed paradigm. There are so many security issues in many aspects. Moreover many IoT devices are connected at fog and they generate much data, therefore light and efficient security mechanism is needed. For example, with inappropriate encryption or authentication algorithm, it causes a huge bandwidth loss. In this paper, we consider issues related with data encryption and authentication mechanism in the network architecture for cloud and fog-based M2M (Machine to Machine) IoT services. This includes trusted encryption and authentication algorithm, and key generation method. The contribution of this paper is to provide efficient security mechanisms for the proposed service architecture. We implemented the envisaged conceptual security check mechanisms and verified their performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.191-196
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• 2024

Recently, research on intelligent IoT technology has been actively conducted by various companies and research institutes to analyze various data collected from IoT devices and provide it through actual application services. However, security issues such as personal information leakage may arise in the process of transmitting and receiving data to use data collected from IoT devices for research and development. In addition, as data collected from multiple IoT devices increases, data management difficulties exist, and data movement is costly and time consuming. Therefore, in this paper, we intend to develop an Ethereum network-based federated learning system with guaranteed reliability to improve security issues and inefficiencies in a federated learning environment composed of various devices.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.54-56
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• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.59-63
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• 2023

Various devices have been emerging as a means of measuring indoor air quality, and among them, there are devices that support real-time remote monitoring through IoT technology and a cloud environment. To improve indoor air quality, based on the results determined by measuring devices, air purifiers or ventilation systems may need to be operated, and temperature and humidity control may be required. In this paper, we propose a design of indoor air quality measuring devices required for indoor air quality evaluation, and of the system needed to control relevant devices to improve indoor air quality through the interaction with the measuring devices. Currently, the servers for the interaction of indoor air quality devices and IoT devices are divided into conventional server type and serverless type, comparing the differences in response time of IoT devices to changes of indoor air quality.

• International Journal of Contents
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• v.13 no.1
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• pp.53-61
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• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.129-137
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• 2015

Various personalized services are provided based on user context these days, and IoT(Internet of Things) devices provides effective ways to collect user context. For example, user's activity such as walking steps, calories, and sleeping hours can be collected using smart activity tracker. Smart scale can sense change of user's weight or body fat percentage. However, these services are independent to each other and not easy to make them collaborate. Many standard bodies are working on the documents for this issue, but due to diversity of IoT use case scenarios, it seems that multiple IoT technologies co-exist for the time being. This paper propose a framework to collaborate heterogeneous IoT services. The proposed framework provides methods to build application for heterogeneous IoT devices and user context management in more intuitive way using HTTP. To improve compatibility and usability, gathered user contexts are based on MPEG-UD. Implementation of framework and service with real-world devices are also presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.444-461
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• 2016

A power-saving mechanism for smartphone devices is developed by analyzing the features of data that are received from Internet of Things (IoT) sensors devices to optimize the data processing policies. In the proposed GreenIoT architecture for power-saving in IoT, the power saving and feedback mechanism are implemented in the IoT middleware. When the GreenIoT application in the power-saving IoT architecture is launched, IoT devices collect the sensor data and send them to the middleware. After the scanning module in the IoT middleware has received the data, the data are analyzed by a feature evaluation module and a threshold analysis module. Based on the analytical results, the policy decision module processes the data in the device or in the cloud computing environment. The feedback mechanism then records the power consumed and, based on the history of these records, dynamically adjusts the threshold value to increase accuracy. Two smart living applications, a biomedical application and a smart building application, are proposed. Comparisons of data processed in the cloud computing environment show that the power-saving mechanism with IoT architecture reduces the power consumed by these applications by 24% and 9.2%.