• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.1-18
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• 2020

The Internet of things (IoT) has been applied to a variety of industrial uses such as public service sectors, medical industries, automotive industries, and so on. Led by smart cities, this is typical. However, from a logistics perspective, the level of application is insufficient. This study examines the applicability of IoT-related technology in a container terminal, an object of the present invention, to derive an applicable plan. Analytic network process (ANP) analysis reveals the following results for IoT applications in container terminals: operating systems (26.7%), safety/environmental/security systems (26.4%), equipment maintenance systems (25.3%), and facility maintenance systems (21.6 %). The second ANP analysis reveals the following results: Economy (40.2%), productivity (21.1%), service level (19.5%), and utilizing technology level (19.2%). The application or standard of evaluation is important when applying IoT technology to container terminals; however, it is not concentrated in a certain area. It is desirable to build each container system with linkage and efficiency from a macroscopic view.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.46-54
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• 2018

The Internet of Things (IoT) is an environment in which various devices provide services to users through communications. Because of the nature of the IoT, data are stored and distributed in heterogeneous information systems. In this situation, IoT end applications should be able to access data without having information on where the data are or what the type of storage is. This mechanism is called Service Discovery (SD). However, some problems arise, since the current SD architectures search for data in physical devices. First, turnaround time increases from searching for services based on physical location. Second, there is a need for a data structure to manage devices and services separately. These increase the administrator's service configuration complexity. As a result, the device-oriented SD structure is not suitable to the IoT. Therefore, we propose an SD structure called Name-based Service-centric Service Discovery (NSSD). NSSD provides name-based centralized SD and uses the IoT edge gateway as a cache server to speed up service discovery. Simulation results show that NSSD provides about twice the improvement in average turnaround time, compared to existing domain name system and distributed hash table SD architectures.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1069-1073
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• 2016

The Internet of Things (IoT) is the interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure. IoT is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine communications and covers a variety of protocols, domains, and applications. Key system-level features that IoT needs to support can be summarized as device heterogeneity, scalability, ubiquitous data exchange through proximity wireless technologies, energy optimized solutions, localization and tracking capabilities, self-organization capabilities, semantic interoperability and data management, embedded security and privacy-preserving mechanisms. Time information is a critical piece of infrastructure for any distributed system. Time information and time synchronization are also fundamental building blocks in the IoT. The IoT requires new paradigms for combining time and data. This paper reviews conventional time keeping mechanisms in the Internet and presents issues to be considered for combining time and data in the IoT.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Smart Media Journal
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• v.12 no.4
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• pp.19-26
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• 2023

IoT edge services utilizing neuromorphic hardware architectures are suitable for autonomous IoT applications as they perform intelligent processing on the device itself. However, spiking neural networks applied to neuromorphic hardware are difficult for IoT developers to comprehend due to their complex structures and various hyper-parameters. In this paper, we propose a method for generating spiking neural network (SNN) models that satisfy user performance requirements while considering the constraints of neuromorphic hardware. Our proposed method utilizes previously trained models from pre-processed data to find optimal SNN model parameters from profiling data. Comparing our method to a naive search method, both methods satisfy user requirements, but our proposed method shows better performance in terms of runtime. Additionally, even if the constraints of new hardware are not clearly known, the proposed method can provide high scalability by utilizing the profiled data of the hardware.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.93-98
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• 2015

IoT (Internet of Things) is recently burgeoning as business applications as well as ICT itself. Among the business applications of IoT, wearable devices are recognized as a leading area of customer devices. This research first identifies customer needs of activity trackers (fitness trackers), as one of representative wearable devices, and mapping the identified needs with the well-known marketing model of marketing mix (4 P's: Product, Price, Promotion, and Place). Survey was applied to university students for identifying current and potential needs for activity trackers. The needs were classified by 4 P's, and according to the results, different from other IT devices, activity trackers has more potential needs. Moreover, reliable distribution channels, offline and company owned shops were preferred, rather than online shopping mall by third parties. The results would provide some valuable implications to not only designers of activity trackers but also business management.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.