• 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 Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1276-1295
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• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.114-122
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• 2024

This study aimed to identify the multiple mediation effects of job satisfaction and job commitment on the relationships between job stress and workers' safety behavior in terms of compliance and participation, in which the multiple mediation effects are a hybrid of parallel and serial mediating relationships. The multiple mediation model was analyzed using the bootstrapping method through the PROCESS macro tool in SPSS. The results showed that job stress negatively affects job satisfaction, job commitment, and workers' safety behavior, and the relationship between job stress and safety behavior is mediated by both job satisfaction and job commitment. The serial mediation effects of job satisfaction and job commitment were also found to be statistically significant in the regression relationship between job stress and safety behavior. Further analysis of the compliance and participation subdimensions of safety behavior showed similar results. Specifically, the serial mediation effects of job satisfaction and job commitment on participation and compliance behavior were further supported; however, the mediation effect of job satisfaction was not significant, whereas that of job commitment did remain significant. Further research is needed to determine if the mediation effect of job satisfaction found in this study can be extended and generalized to workers in various fields and industries.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.595-604
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• 2009

The HCCA reserves the channel resources based on the mean data rate in IEEE 802.11e. It may cause either the waste of channel resource or the increase of transmission delay at MAC layer if the frame size is rapidly varied when a compressed mode video codec such as MPEG video is used. To solve these problems, it is developed that the packet scheduler allocates the wireless resource adaptation by according to the packet size. However, it is difficult to perform the admission control because of the difficulty with calculating the available resources. In this paper, we propose a CAC mechanism to solve the problem that may not satisfy the QoS by increasing traffic load in case of using EDCA. Especially, the proposed CAC mechanism calculates the EB of TSs using the traffic information transmitted by the application layer and the number of average transmission according to the wireless channel environment, and then determines the admission of the TS based on the EB. According to the simulation results of the proposed CAC mechanism, it admitted the TSs under the loads which are satisfied within the delay bound. Therefore, the proposed mechanism guarantees QoS of streaming services effectively.

• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.31-56
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• 2013

The purpose of this study was to investigate the changes that family caregivers have experienced since using the Korean Long-Term Care Insurance(LTCI) system. In-depth interviews were conducted to determine how the services offered within the LTCI program had affected family caregiving and what changes they had incurred. Results from the qualitative content analysis show that the LTCI program significantly reduced the caregiving burden among family caregivers although burdens that family caregiver perceived varied greatly depending on the types of service that the family selected, and assigned family caregivers different identities and diverse roles(i.e., service user, family caregiver, certified care provider) depending on the service they use. The phenomenon of 'certified family care provider', which was not an intention of LTCI, demonstrates the practical need of elderly persons who require both care and the comfort of family and economic status of the family. Despite the positive impact of the LTCI policy on the family caregivers' burden and family relationship, the current LTCI system should be modified in order to better meet the needs of beneficiaries and their family caregivers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.805-813
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• 2018

The research was conducted with the purpose of providing effective safety and quality control system for assistive products for handicapped those are used extensively. Assistive products couldn't be classified independently due to collision with the act of medical device and lack in legal basis. The issues about safety and quality have been solved by other legal frames on a case by case basis. We couldn't find any abroad case of independent safety and quality control policy. For the practical solution, this article suggested hybrid classification system mixed with existing policies. Each classified branches are allocated to the appropriate policy of safety and quality control so those are ease of understanding and prospect. And also a delicacy process was suggested not to leave off any assistive products. Through these suggests of the improvement it is expected that blind areas of safety and quality control for assistive products for handicapped could be solved and identity of assistive products could be established to provide product safety for handicapped and boost relevant industries.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.4
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• pp.344-350
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• 2009

This paper proposes a hybrid antenna with novel beam steering scheme. The antenna have a cassegrain structure composed of two reflectors. The main reflector is designed for high gain performance using parabola curvature, and the sub-reflector is plate and can be rotated by ${\pm}3^{\circ}$. Thus proposed antenna can steer a antenna beam using the inclination of sub-reflector. A feed array composed of 20 elements is adapted as a feeder for electrical beam steering, and the antenna can be possible to steer the beam by the feed array with sub-reflector. Proposed antenna was fabricated to be operated in Ka-band(30.085$\sim$30.885 GHz) for TX and K-band(20.355$\sim$21.155 GHz), which are the operation frequencies of the Korean satellite, Mugunhwa, to provide satellite multi-media service to vehicles. By the performance test, it can be known that the antenna has minimum gain of 47 dBi for TX and 44.4 dBi for TX and can steer the beam by ${\pm}2^{\circ}$ with sub-reflector.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.483-494
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• 2005

The standard streaming delivery Is mostly based on UDP with no end-to-end congestion control. For this reason, wide usage of multimedia applications in Internet might lead to congested networks. To avoid such a situation, studies on the congestion controlled streaming delivery has been increasingly done after the 1990s. However, by considering only the stability aspect of network, these works ignore the characteristics of multimedia streaming applications. Moreover, most of previous works have no consideration on the network delay which produces an effect on streaming service. In this thesis, in order to overcome limitations of the previous transmission schemes for streaming, we propose a new transmission scheme called 'BEST(Buffer-driven Efficient STreaming)'. The BEST takes a hybrid approach that considers both user-level requirements and network-level requriements. Therefore, the BEST improves the stability of networks by adjusting the sending rate suitable for network status and it also provides the smoothed playback by preventing buffer underflow or overflow. The BEST is designed to consider high-delay networks. Through the simulation, we prove that the BEST satisfies both user-level and network-level requirements in a high-delay network environments.