• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.304-309
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• 2019

This paper presents a CMOS RF-to-DC converter for video surveillance disposable IoT applications. It widely harvests RF energy of 3G/4G cellular low-band frequency range by employing a tunable impedance matching network. The proposed converter consists of the differential-drive cross-coupled rectifier and the matching network with a 4-bit capacitor array. The proposed converter is designed using 130-nm standard CMOS process. The designed energy harvester can rectify the RF signals from 700 MHz to 900 MHz. It has a peak RF-to-DC conversion efficiency of 72.25%, 64.97%, and 66.28% at 700 MHz, 800 MHz, and 900 MHz with a load resistance of 10kΩ, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.80-90
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• 2006

WSN(Wireless Sensor Network) performs to detect and collect environmental information for one purpose. As examples, WSN is applicable for home network, patient management of a hospital, logistics management, status detection during the war and so on. The WSN is composed of a sink node and several sensor nodes and has a constraint in an aspect of energy consumption caused by limited battery resource. So many required mechanisms in WSN should consider the remained energy condition. A routing mechanism is requested to deliver the collected information to a sink considering energy efficiency in WSN. There have been many researches to establish (a) route(s) for data delivery to the sink. In this paper, we propose establishment of efficient routes. We proposed a uniform routing mechanism together with considering energy efficiency. For the routing, we define energy probability as routing metrics information and Performs suppression of exchange of control messages. In addition, we derive to uniformly consume the energy of the sensor node when establishing the routes. Also, we evaluate and analyze the energy efficiency for proposed mechanisms through NS-2 simulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.626-648
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• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.57-60
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• 2015

Recently, a multi-user massive MIMO (MU-Massive MIMO) network has been attracting tremendous interest as one of technologies to accommodate explosively increasing mobile data traffic. The MU-Massive MIMO network can significantly enhance the network capacity because a base station (BS) equipped with large-scale transmit antennas can transmit high-rate data to multiple users simultaneously. In the MU-Massive MIMO network, transmit antenna selection schemes are generally used to decrease the computational complexity and cost of the BS. In this paper, we investigate the energy efficiency of the transmit antenna selection scheme in the MU-Massive MIMO network and the optimal number of selected transmit antennas for maximizing the energy efficiency.

• IE interfaces
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• v.23 no.2
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• pp.108-117
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• 2010

Wireless sensor network may be installed where it is hard to access or where one has to constantly gather data without any human's monitoring. Sensors which compose a sensor network are usually small with poor battery capacities and thus energy-efficient usage is very important because of difficulties in replacing or recharging the batteries to make the lifetime of the whole sensor network longer. LEACH protocol, a well-known hierarchical routing protocol, may resolve this problem by distributing the role of cluster header evenly to the sensor nodes in the whole area of network at each round. In this thesis, we introduce a variant of LEACH protocol which considers the distance between base station and sensors in the way that if a node is far from the base station then the probability that this node becomes a cluster header is low. Experiments to evaluate the energy efficiency and the ability to collect the information show that the proposed method in this paper has maintained much wider sensing area while keeping the energy efficiency same as LEACH.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.136-141
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• 2021

Research on wireless sensor networks has focused on the monitoring and characterization of large-scale physical environments and the tracking of various environmental or physical conditions, such as temperature, pressure, and wind speed. We propose a stochastic mobility model that can be applied to a MANET (Mobile Ad-hoc NETwork). environment, and apply this mobility model to a newly proposed clustering-based routing protocol. To verify its stability and durability, we compared the proposed stochastic mobility model with a random model in terms of energy efficiency. The FND (First Node Dead) was measured and compared to verify the performance of the newly designed protocol. In this paper, we describe the proposed mobility model, quantify the changes to the mobile environment, and detail the selection of cluster heads and clusters formed using a fuzzy inference system. After the clusters are configured, the collected data are sent to a base station. Studies on clustering-based routing protocols and stochastic mobility models for MANET applications have shown that these strategies improve the energy efficiency of a network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2257-2264
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• 2011

Sensor nodes in Wireless sensor network have limited resources and consume almost all energy to the communication. For its traffic feature as a burst traffic type toward a sink node, it has high probability to network congestion. Network congestion causes packet drops and retransmission of dropped packets draws energy consumption. In particular, the loss of packet that is from the sensor node far away from a sink node requires additional energy consumption by frequent retransmission. This paper presents a traffic control mechanism that determines packet transfer by considering priority of packet and congestion level as well as hop count. Analysis of proposed mechanism by simulation demonstrated that it improved energy efficiency.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.4
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• pp.87-94
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• 2016

This paper proposes an energy transmission method to maximize energy efficiency of a based station. This method makes use of classification of service type to solve an inefficient use of transmission power, which is from exponential relationship between the legacy data throughput and transmission power. The proposed one is a way to find the most energy-efficiency points with the transmitted optimal amount of data on users in a base station of wireless network environment. For this, we propose EETA (Energy-Efficient Transmission Algorithm) which can control the amount of data and the holding time at the base station. As a result, the proposed method can improve the energy efficiency of about 10% compared to the legacy base station.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.31-38
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• 2021

The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R² value of the artificial neural network and random forest algorithms is observed to be more than 0.9.

• ETRI Journal
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• v.44 no.6
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• pp.885-902
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• 2022

The mmWave cell-free massive MIMO (CFmMIMO), combining the advantages of wide bandwidth in the mmWave frequency band and the high- and uniform-spectral efficiency of CFmMIMO, has recently emerged as one of the enabling technologies for 6G. In this paper, we propose a novel framework for energy-efficient mmWave CFmMIMO systems that uses low-resolution digital-analog converters (DACs) and phase shifters (PSs) to introduce lowcomplexity hybrid precoding. Additionally, we propose a heuristic pilot allocation scheme that makes the best effort to slash some interference from copilot users. The simulation results show that the proposed hybrid precoding and pilot allocation scheme outperforms the existing schemes. Furthermore, we reveal the relationship between the energy and spectral efficiencies for the proposed mmWave CFmMIMO system by modeling the whole network power consumption and observe that the introduction of low-resolution DACs and PSs is effective in increasing the energy efficiency by compromising the spectral efficiency and the network power consumption.