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Eui-Nam Huh

Bio: Eui-Nam Huh is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Cloud computing & Wireless sensor network. The author has an hindex of 33, co-authored 377 publications receiving 5404 citations. Previous affiliations of Eui-Nam Huh include University of Maryland University College.


Papers
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Proceedings ArticleDOI
27 Aug 2014
TL;DR: This paper has discussed the IoT-Cloud computing integration in detail in detail and presented the architecture of Smart Gateway with Fog Computing, which has tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data upload Delay, and Bulk- data synchronization delay.
Abstract: With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) are gaining importance. In IoTs, literally anything can be part of it, whether it is sensor nodes or dumb objects, so very diverse types of services can be produced. In this regard, resource management, service creation, service management, service discovery, data storage, and power management would require much better infrastructure and sophisticated mechanism. The amount of data IoTs are going to generate would not be possible for standalone power-constrained IoTs to handle. Cloud computing comes into play here. Integration of IoTs with cloud computing, termed as Cloud of Things (CoT) can help achieve the goals of envisioned IoT and future Internet. This IoT-Cloud computing integration is not straight-forward. It involves many challenges. One of those challenges is data trimming. Because unnecessary communication not only burdens the core network, but also the data center in the cloud. For this purpose, data can be preprocessed and trimmed before sending to the cloud. This can be done through a Smart Gateway, accompanied with a Smart Network or Fog Computing. In this paper, we have discussed this concept in detail and present the architecture of Smart Gateway with Fog Computing. We have tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data Upload Delay, and Bulk-data Synchronization Delay.

543 citations

Proceedings ArticleDOI
27 Mar 2014
TL;DR: IoT and cloud computing integration is not that simple and bears some key issues, so key issues along with their respective potential solutions have been highlighted in this paper.
Abstract: With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.

394 citations

Proceedings ArticleDOI
24 Mar 2015
TL;DR: This paper provides an effective and efficient resource management framework for IoTs, which covers the issues of resource prediction, customer type based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics.
Abstract: Pervasive and ubiquitous computing services have recently been under focus of not only the research community, but developers as well. Prevailing wireless sensor networks (WSNs), Internet of Things (IoT), and healthcare related services have made it difficult to handle all the data in an efficient and effective way and create more useful services. Different devices generate different types of data with different frequencies. Therefore, amalgamation of cloud computing with IoTs, termed as Cloud of Things (CoT) has recently been under discussion in research arena. CoT provides ease of management for the growing media content and other data. Besides this, features like: ubiquitous access, service creation, service discovery, and resource provisioning play a significant role, which comes with CoT. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework for IoTs, which we provide in this paper. Our model covers the issues of resource prediction, customer type based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics. The implementation was done using Java, while the model was evaluated using CloudSim toolkit. The results and discussion show the validity and performance of our system.

318 citations

Proceedings ArticleDOI
23 Mar 2015
TL;DR: The proposed methodology for resource estimation and management has taken into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probabilities.
Abstract: Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core network and the cloud. For this purpose, Fog computing plays an important role. Fog resides between underlying IoTs and the cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. For this purpose, Fog requires an effective and efficient resource management framework, which we provide in this paper. Moreover, since Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types, having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can quit resource utilization at any moment. In our proposed methodology for resource estimation and management, we have taken into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. Implementation of our system was done using Java, while evaluation was done on CloudSim toolkit. The discussion and results show that these factors can help service provider estimate the right amount of resources, according to each type of service customers.

188 citations

Proceedings ArticleDOI
15 Feb 2009
TL;DR: This paper proposes a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications and discusses issues and proposed reasonable solutions to enable this framework.
Abstract: In the past few years, wireless sensor networks (WSNs) have been gaining increasing attention because of their potential of enabling of novel and attractive solutions in areas such as industrial automation, environmental monitoring, transportation business, health-care etc. If we add this collection of sensor derived data to various Web-based social networks or virtual communities, blogs etc., we can have a remarkable transformation in our ability to "see" ourselves and our planet. Our primary goal is to facilitate connecting sensors, people and software objects to build community-centric sensing applications. However, the computational tools needed to launch this exploration may be more appropriately built from the data center "Cloud" computing model than the traditional HPC approaches. In this paper, we propose a framework to enable this exploration by integrating sensor networks to the emerging data center "cloud" model of computing. But there are many challenges to enable this framework. We propose a pub-sub based model which simplifies the integration of sensor networks with cloud based community-centric applications. Also there is a need for internetworking cloud providers in case of violation of service level agreement with users. We discussed these issues and proposed reasonable solutions.

181 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Book ChapterDOI
01 Jan 2019
TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
Abstract: Fog computing extends the cloud computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are 1) low latency and location awareness, 2) widespread geographical distribution, 3) mobility, 4) very large number of nodes, 5) predominant role of wireless access, 6) strong presence of streaming and real time applications, and 7) heterogeneity. In this chapter, the authors argue that the above characteristics make the Fog the appropriate platform for a number of critical internet of things (IoT) services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).

2,384 citations

Journal ArticleDOI
TL;DR: This paper provides an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges, and identifies open issues and future directions in this field, which it expects to play a leading role in the landscape of the Future Internet.

1,880 citations

Journal ArticleDOI
TL;DR: Computer and Robot Vision Vol.
Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

1,426 citations