• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.985-996
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• 2006

As the internet technologies develop, the geographic information system environment is changing to the web-based service. Since geospatial information of the existing Web-GIS services were developed independently, there is no interoperability to support diverse map formats. In spite of the same geospatial information object it can be used for various proposes that is duplicated in GIS separately. It needs intelligent strategies for optimal replica selection, which is identification of replication geospatial information objects. And for management of replication objects, OMG, GLOBE and GRID computing suggested related frameworks. But these researches are not thorough going enough in case of geospatial information object. This paper presents a model of location service, which is supported for optimal selection among replication and management of replication objects. It is consist of tree main services. The first is binding service which can save names and properties of object defined by users according to service offers and enable clients to search them on the service of offers. The second is location service which can manage location information with contact records. And obtains performance information by the Load Sharing Facility on system independently with contact address. The third is intelligent selection service which can obtain basic/performance information from the binding service/location service and provide both faster access and better performance characteristics by rules as intelligent model based on rough sets. For the validity of location service model, this research presents the processes of location service execution with Graphic User Interface.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.12-21
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• 2012

IEEE 802.11 MAC protocol standard, DCF(CSMA/CA), is originally designed to ensure the fair channel access between mobile nodes sharing the local wireless channel. It has been, however, revealed that some misbehavior nodes transmit more data than other nodes through artificial means in hot spot area spreaded rapidly. The misbehavior nodes may modify the internal process of their MAC protocol or interrupt the MAC procedure of normal nodes to achieve more data transmission. This problem has been referred to as a selfish node problem and almost literatures has proposed methods of analyzing the MAC procedures of all mobile nodes to detect the selfish nodes. However, these kinds of protocol analysis methods is not effective at detecting all kinds of selfish nodes enough. This paper address this problem of detecting selfish node using Tsallis-Entropy which is a kind of statistical method. Tsallis-Entropy is a criteria which can show how much is the density or deviation of a probability distribution. The proposed algorithm which operates at a AP node of wireless LAN extracts the probability distribution of data interval time for each node, then compares the one with a threshold value to detect the selfish nodes. To evaluate the performance of proposed algorithm, simulation experiments are performed in various wireless LAN environments (congestion level, how selfish node behaviors, threshold level) using ns2. The simulation results show that the proposed algorithm achieves higher successful detection rate.

• CRM연구
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• v.3 no.2
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• pp.43-68
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• 2010

One of the recent research trends that universities are increasingly adopting the concept of 'customer' and the customer-oriented strategy has urged us to research enterprise-wide CRM strategy adaptable to university administration. As the first step of CRM strategy for university management, we try to validate the difference of CRM strategic factors among university types. Drawing upon both CRM process and customer equity drivers, which have been recognized as core frameworks for CRM strategy, we developed those survey instruments adoptable into university industry, and validated statistically-significant difference among 12 types of university group constructed by the levels of university evaluation and the location of the universities. We collected 261 responses from 177 universities from all over the country and analyzed the data to see the levels of CRM processes consisting of customer acquisition, retention, and expansion, and customer equity drivers consisting of value equity, brand equity, and relationship equity by using multivariate ANOVA(MANOVA). The result confirms the explicit differences of the levels of CRM processes and customer equity drivers between the groups by university evaluation levels(high/middle/low). However, the analysis failed to show the significant differences of those between the group by university locations(the capital/the suburbs/the six megalopolises/other countries). More specifically, the level of activities for customer acquisition and retention of the universities in the higher-graded group are significantly different from those in the lower-graded group from the perspective of CRM process. In terms of customer equity drivers, the levels of both brand equity and relationship equity of the higher-graded group are significantly higher than those of both middle and lower-graded group. In addition, we found that the value equity between the higher and lower-graded groups, and the brand equity between the middle and lower-graded groups are different each other. This study provides an important meaning in that we tried to consider CRM strategy which has been mainly addressed in profit-making industries in terms of non-profit organization context. Our endeavors to develop and validate empirical measurements adoptable to university context could be an academic contribution. In terms of practical meaning, the processes and results of this study might be a guideline to many universities to build their own CRM strategies. According to the research results, those insights could be expressed in several messages. First, we propose to universities that they should plan their own differentiated CRM strategies according to their positions in terms of university evaluation. For example, although it is acceptable that a university in lower-level group might follow the CRM process strategy of the middle-level group universities, it is not a good idea to imitate the customer acquisition and retention activities of the higher-level group universities. Moreover, since this study reported that the level of universities' brand equity is just correlated with the level of university evaluation, it might be pointless for the middle or lower-leveled universities if they just copy their brand equity strategies from those of higher-leveled ones even though such activities are seemingly attractive. Meanwhile, the difference of CRM strategy by university position might provide universities with the direction where they should go for their CRM strategies. For instance, our study implies that the lower-positioned universities should improve all of the customer equity drivers with concerted efforts because their value, brand, and relationship equities are inferior compared with the higher and middle-positioned universities' ones. This also means that they should focus on customer acquisition and expansion initiatives rather than those for customer retention because all of the customer equity drivers could be influenced by the two kinds of CRM processes (KIm and Lee, 2010). Surely specific and detailed action plans for enhancing customer equity drivers should be developed after grasping their customer migration patterns illustrated by the rates of acquisition, retention, upgrade, downgrade, and defection for each customer segment.