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Author

Minchul Shin

Bio: Minchul Shin is an academic researcher from Hanyang University. The author has contributed to research in topics: Wireless sensor network & Sensor node. The author has an hindex of 5, co-authored 7 publications receiving 88 citations.

Papers
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Journal ArticleDOI
TL;DR: An energy prediction algorithm that uses the light intensity of fluorescent lamps in an indoor environment to accurately estimate the amount of energy that will be harvested by a solar panel using a weighted average for light intensity is proposed.
Abstract: The solar powered energy harvesting sensor node is a key technology for Internet of Things (IoT), but currently it offers only a small amount of energy storage and is capable of harvesting only a trivial amount of energy. Therefore, new technology for managing the energy associated with this sensor node is required. In particular, it is important to manage the transmission interval because the level of energy consumption during data transmission is the highest in the sensor node. If the proper transmission interval is calculated, the sensor node can be used semi-permanently. In this study, the authors propose an energy prediction algorithm that uses the light intensity of fluorescent lamps in an indoor environment. The proposed algorithm can be used to accurately estimate the amount of energy that will be harvested by a solar panel using a weighted average for light intensity. Then, the optimal transmission interval is calculated using the amount of predicted harvested energy and residual energy. The results from the authors' experimental testbeds show that their algorithm's performance is better than the existing approaches. The energy prediction error of their algorithm is approximately 0.5%.

47 citations

Proceedings ArticleDOI
09 Jan 2010
TL;DR: The proposed mobility-based prediction algorithm with dynamic LGD (Link Going Down) triggering for vertical handover by applying the IS (Information Server) of IEEE 802.21 MIH (Media Independent Handover) can reduce handover latency for MIPv6 (Mobile IPv6), FMIPv 6 (Fast Handover for Mobile IPv6) by advancing the LGD trigger point.
Abstract: In this paper, we propose a mobility-based prediction algorithm with dynamic LGD (Link Going Down) triggering for vertical handover by applying the IS (Information Server) of IEEE 802.21 MIH (Media Independent Handover). The proposed algorithm predicts a possible moving area (PMA) of the mobile terminal based on mobility information (the velocity, coordinate values, position, movement detection, etc) in IS. Since the PMA indicates a next target cell for handover, it can adnnce the LGD trigger point dynamically to prepare for handover beforehand. The analytical results show that our prediction algorithm can reduce handover latency for MIPv6 (Mobile IPv6), FMIPv6 (Fast Handover for Mobile IPv6) by advancing the LGD trigger point.

15 citations

Book ChapterDOI
16 Dec 2012
TL;DR: High-availability Seamless Redundancy (HSR) protocol is proposed to recover disconnection of network within a short time and has a problem that causes the unnecessary traffic.
Abstract: High-availability Seamless Redundancy (HSR) protocol is proposed to recover disconnection of network within a short time. An Intelligent Electronic Device (IED) within a ring topology transmits two identical frames to the destination IED through both of the ports. This means that even in the case of disconnection of network, there is no stoppage of network operations whatsoever. However, because two identical frames are circulated inside the network, HSR protocol has a problem that causes the unnecessary traffic. This problem will degrade the network performance and may cause network congestion or delays.

12 citations

Proceedings ArticleDOI
01 Jul 2015
TL;DR: This work proposes a novel indoor localization system consisting of a preprocessing method and a post-processing method to improve channel stability and location accuracy in RSSI-based localization and proves the performance of this system by conducting experiments in a real indoor environment.
Abstract: An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

10 citations

Proceedings Article
11 May 2010
TL;DR: Analytical results show that a mobile cluster applied to a vehicle can perform data transmission using less power than direct communication applied to the vehicle.
Abstract: Significant research is being performed in order to provide advanced vehicular communication systems. In this paper, we propose a mobile cluster that supports the mobility and low-power requirements of sensor nodes attached to a vehicle. Vehicular wireless sensor networks consist of cluster-based grids. The proposed mobile cluster is applied to a vehicle equipped with a sensor node, and consists of a mobile cluster head (MCH) and mobile cluster members (MCMs). The MCMs transmit the sensed data to the sink node through the MCH. The sink node knows the position of the mobile cluster by managing the ID change of the mobile cluster, and the sink node or the sensor node of the vehicular wireless sensor network can transmit data to the mobile cluster. On the other hand, in the case of direct communication, because the sensor node attached to the vehicle operates independently, each sensor node transmits data to the sink node, resulting in heavy energy consumption. The analytical results show that a mobile cluster applied to the vehicle can perform data transmission using less power than direct communication applied to the vehicle.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of VHO techniques, along with the main algorithms, protocols and tools proposed in the literature are presented, and the most appropriate V HO techniques to efficiently communicate in VN environments are suggested considering the particular characteristics of this type of networks.

229 citations

Journal ArticleDOI
TL;DR: The security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.
Abstract: With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user’s privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.

141 citations

Journal ArticleDOI
TL;DR: This work presents energy-harvesting and sub-systems for IoT networks, and highlights future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.
Abstract: An increasing number of objects (things) are being connected to the Internet as they become more advanced, compact, and affordable. These Internet-connected objects are paving the way toward the emergence of the Internet of Things (IoT). The IoT is a distributed network of low-powered, low-storage, light-weight and scalable nodes. Most low-power IoT sensors and embedded IoT devices are powered by batteries with limited lifespans, which need replacement every few years. This replacement process is costly, so smart energy management could play a vital role in enabling energy efficiency for communicating IoT objects. For example, harvesting of energy from naturally or artificially available environmental resources removes IoT networks’ dependence on batteries. Scavenging unlimited amounts of energy in contrast to battery-powered solutions makes IoT systems long-lasting. Thus, here we present energy-harvesting and sub-systems for IoT networks. After surveying the options for harvesting systems, distribution approaches, storage devices and control units, we highlight future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.

98 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes by utilizing ambient solar photovoltaic energy.
Abstract: The Wireless Sensor Networks (WSN) are the basic building blocks of today’s modern internet of Things (IoT) infrastructure in smart buildings, smart parking, and smart cities The WSN nodes suffer from a major design constraint in that their battery energy is limited and can only work for a few days depending upon the duty cycle of operation The main contribution of this research article is to propose an efficient solar energy harvesting solution to the limited battery energy problem of WSN nodes by utilizing ambient solar photovoltaic energy Ideally, the Optimized Solar Energy Harvesting Wireless Sensor Network (SEH-WSN) nodes should operate for an infinite network lifetime (in years) In this paper, we propose a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes The research focus is to increase the overall harvesting system efficiency, which further depends upon solar panel efficiency, PWM efficiency, and MPPT efficiency Several models for solar energy harvester system have been designed and iterative simulations were performed in MATLAB/SIMULINK for solar powered DC-DC converters with PWM and MPPT to achieve optimum results From the simulation results, it is shown that our designed solar energy harvesting system has 87% efficiency using PWM control and 96% efficiency ( η s y s ) by using the MPPT control technique Finally, an experiment for PWM controlled SEH-WSN is performed using Scientech 2311 WSN trainer kit and a Generic LM2575 DC-DC buck converter based solar energy harvesting module for validation of simulation results

54 citations

Journal ArticleDOI
TL;DR: In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices and it is proved that the proposed model performs better than other state-of-art regression algorithms in preserving the batterylife of IoT Devices.
Abstract: The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real-world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on ‘Beach Water Quality – Automated Sensors’ data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre-processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state-of-art regression algorithms in preserving the battery life of IoT devices.

53 citations