• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.36-40
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• 2019

Energy harvesting is an advantageous technology for wireless sensor networks (WSNs) that dispenses with the need for periodic replacement of batteries. WSNs are composed of numerous sensors for the collection of data and communication; hence, they are important in the Internet of Things (IoT). However, due to low power generation and energy conversion efficiency, harvesting technologies have so far been utilized in limited applications. In this study, a piezoelectric energy harvester was modeled in a vibration environment. This harvester has an oval-shaped configuration as compared to the conventional cantilever-type piezoelectric energy harvester. An analytical model based on an equivalent circuit was developed to appraise the advantages of the oval-shaped piezoelectric energy harvester in which several structural parameters were optimized for higher output performance in given vibration environments. As a result, an oval-shaped energy harvester with an average output power of 2.58 mW at 0.5 g and 60 Hz vibration conditions was developed. These technical approaches provided an opportunity to appreciate the significance of autonomous sensor networks.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.201-202
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• 2018

자동차 부품산업은 자동차산업의 후방산업으로 완성차 메이커 제조산업의 조립용인 중간재 산업으로 부품의 품질에 따라 완성차의 성능이 좌우된다. 자동차 산업구조상 완성차에 종속적인 사업구조로 부품사의 독자 성장이 어렵고 수익성이 완성차업체에 종속 된다. 자동차부품사의 대형화 및 생산공정 자동화 변화로 기존의 수직계열화된 부품 공급 관계에도 변화가 예상되며, 완성차업체는 통합적인 시스템 부품을 생산할 수 있는 글로벌 대형 부품업체에 의존하는 상황이 전개될 수도 있다. 이와 같은 위기와 변화를 극복하기 위해 본 연구에서는 산업구조분석을 자동차 부품산업에 적용하여 자동차부품산업의 경쟁력제고 전략을 도출하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.111-112
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• 2019

Edge computing 환경에서는 노드끼리 직접 또는 간접적으로 전송되는 많은 수의 데이터가 Computing 노드에 의해 수집된다. Computing 노드에 실시간 적으로 전송되어지는 데이터의 저장 및 처리를 위해 기계학습(Machine learning) 기법이 사용된다. 기존의 기계학습 모델의 학습방법의 경우 Edge computing 노드의 지능화에 다소 맞지 않는 방법이며 노드들 간의 협업 시스템을 기계학습 모델에 구축하는 것 또한 중요개선사항 중 하나이다. 본 논문에서는 Edge computing 환경에서 적용 가능한 기계학습 모델을 조사하였다. 본 조사를 통하여 향후 edge computing 환경에서의 제약사항에 대해 더 구체적이며 다양한 연구방향을 제시할 수 있으며 효율적인 모델 적용을 목표로 한다.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.131-137
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• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.67-76
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• 2018

BLE (Bluetooth Low Energy) has been extensively used for communication between mobile applications and wearable devices in IoT (Internet of Things). In developing Android applications, wearable devices, on which the applications can run, should be available because the existing Android SDK does not support any BLE emulation facility. In this study, we have designed and implemented the first Android BLE emulator. Using this, we are able to develop and test BLE-based Android applications even when without wearable devices. We have also proposed an automatic generation method of Android BLE scenarios based on graph model. We have shown that the method is useful for systematically testing BLE application protocols by running the generated scenarios on the Android BLE emulator.

• Information and Communications Magazine
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• v.32 no.11
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• pp.41-49
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• 2015

현대 사회는 스마트폰 사용량의 급증으로 인한 모바일 트래픽의 폭발적인 증가와 IoT(Internet of Things)를 비롯한 융복합 유무선 서비스의 확대 등으로 국가적으로 한정적 자원인 주파수의 효율적 사용을 인한 대책 마련이 시급한 실정이다. 이에 미국 및 유럽을 중심으로 다양한 용도의 무선기기들이 한정적 주파수 대역을 효율적으로 이용하기 위한 방안으로 주파수 공동사용에 대한 정책도입 및 연구개발이 지속적으로 추진되고 있다. 본 고에서는 수요 대비 공급 부족 현상이 점차 심각해 지고 있는 한정적 주파수 자원의 효율적 이용을 위한 대안으로 급부상하고 있는 주파수 공동사용 기술에 대한 국내외 관련 정책 및 기술추진 현황에 대해 소개하고자 한다. 특히 본 고에서는 2010년 국가광대역계획(NBP, National Broadband Plan) 발표를 시작으로 전 세계적으로 주파수 공동사용 추진에 있어 가장 적극적인 행보를 보여주고 있는 미국에서의 3.5GHz 대역 주파수 공동사용을 위한 전파규칙인 CFR(Code of Federal Regulation) Part 96의 주요 내용을 자세히 소개하고자 한다. 본 고의 II장에서는 국내외에서 진행되고 있는 대표적인 주파수 공동사용 정책추진 현황에 대해 소개하였으며, III장에서는 올해 4월 최종 전파규칙이 발표된 미국의 SAS(Spectrum Access System) 기반 주파수 공동사용 전파규칙에 대해 소개하였다. 또한 IV장에서는 SAS 기반의 주파수 공동사용 기법 및 기존에 개발되고 있는 다양한 동적 스펙트럼 접속 기법들을 미국 내 주요 도심지역에 적용하기 위한 대규모 테스트베드 프로그램인'Model City'프로그램 소개 및 결론을 통하여 주파수 공동사용 도입의 당위성을 설명하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2377-2398
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• 2021

In the recent modernized world, utilization of natural resources (renewable & non-renewable) is increasing drastically due to the sophisticated life style of the people. The over-consumption of non-renewable resources causes pollution which leads to global warming. Consequently, government agencies have been taking several initiatives to control the over-consumption of non-renewable natural resources and encourage the production of renewable energy resources. In this regard, we introduce an IoT powered integrated framework called as green home architecture (GHA) for green score calculation based on the usage of natural resources for household purpose. Green score is a credit point (i.e.,10 pts) of a family which can be calculated once in a month based on the utilization of energy, production of renewable energy and pollution caused. The green score can be improved by reducing the consumption of energy, generation of renewable energy and preventing the pollution. The main objective of GHA is to monitor the day-to-day usage of resources and calculate the green score using the proposed green score algorithm. This algorithm gives positive credits for economic consumption of resources and production of renewable energy and also it gives negative credits for pollution caused. Here, we recommend a green score based tax calculation system which gives tax exemption based on the green score value. This direct beneficiary model will appreciate and encourage the citizens to consume fewer natural resources and prevent pollution. Rather than simply giving subsidy, this proposed system allows monitoring the subsidy scheme periodically and encourages the proper working system with tax exemption rewards. Also, our GHA will be used to monitor all the household appliances, vehicles, wind mills, electricity meter, water re-treatment plant, pollution level to read the consumption/production in appropriate units by using the suitable sensors. These values will be stored in mass storage platform like cloud for the calculation of green score and also employed for billing purpose by the government agencies. This integrated platform can replace the manual billing and directly benefits the government.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.