• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.461-468
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• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.663-673
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• 2014

The WBAN technology means a short distance wireless network which provides each device interactive communication by connecting devices inside and outside of body. Standardization on the physical layer, data link layer, network layer and application layer is in progress by IEEE 802.15.6 TG BAN. Wireless body area network is usually configured in energy efficient using sensor and zigbee device due to the power limitation and the characteristics of human body. Wireless sensor network consist of sensor field and sink node. Sensor field are composed a lot of sensor node and sink node collect sensing data. Wireless sensor network has capacity of the self constitution by protocol where placed in large area without fixed position. In this paper, we proposed the efficient addressing scheme for improving the performance of routing algorithm by using ZigBee in WBAN environment. A distributed address allocation scheme used an existing algorithm that has wasted in address space. Therefore proposing x, y and z coordinate axes from divided address space of 16 bit to solve this problems. Each node was reduced not only bitwise but also multi hop using the coordinate axes while routing than Cskip algorithm. I compared the performance between the standard and the proposed mechanism through the numerical analysis. Simulation verified performance about decrease averaging multi hop count that compare proposing algorithm and another. The numerical analysis results show that proposed algorithm reduced the multi hop better than ZigBee distributed address assignment

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1142-1147
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• 2015

As a method to increase the scalability and efficiency of wireless sensor networks, a scheme to construct networks hierarchically has received considerable attention among researchers. Researches on the methods to construct wireless networks hierarchically have been conducted focusing on how to select nodes such that they constitute a backbone network of wireless network. Nodes comprising the backbone network should be connected themselves and can cover other remaining nodes. A problem to find the minimum number of nodes which satisfy these conditions is known as the minimum connected dominating set (MCDS) problem. The MCDS problem is NP-hard, therefore there is no efficient algorithm which guarantee the optimal solutions for this problem at present. In this paper, we propose a novel multi-start local search algorithm to solve the MCDS problem efficiently. For the performance evaluation of the proposed method, we conduct extensive experiments and report the results.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4781-4803
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• 2017

Environmental monitoring systems using Wireless Sensor Networks (WSNs) face the challenge of high power consumption, due to the high levels of multi-hop data communication involved. In order to overcome the issue of fast energy depletion, a proof-of-concept implementation proves that adopting a clustering algorithm in environmental monitoring applications will significantly reduce the total power consumption for environment sensor nodes. In this paper, an energy-efficient WSN-based environmental monitoring system is proposed and implemented, using eight sensor nodes deployed over an area of $1km^2$, which took place in the city of Tabuk in Saudi Arabia. The effectiveness of the proposed environmental monitoring system has been demonstrated through adopting a number of real experimental studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.528-534
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• 2013

The energy consumption problem should be taken into consideration in wireless sensor network. Many studies have been proposed to address the energy consumption and delay problem. In this paper, we propose BISA(Bio-inspired Scheduling Algorithm) to reduce the energy consumption and delay in wireless sensor networks based on biological system. BISA investigates energy-efficient routing path and minimizes the energy consumption and delay using multi-channel for data transmission by multiplexing data transmission path. Through simulation, we confirm that the proposed scheme guarantees the efficient energy consumption and delay requirement.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.97-103
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• 2009

This paper proposes efficient routing scheme named MP-DD(MultiPath-Direct Diffusion) which is simple enough to be applicable to the wireless sensor networks. Conventional DD uses only one optimal path, therefore it consumes more energy of specific nodes. MP-DD uses multiple path and has a information of hop upstream node in the direction of the base-station is determined which are obtainable via the process of self-organization of the network. Simulation results show the feasibility of the simple routing schemes for the sensor networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.472-474
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• 2019

We propose an antenna for subminiature sensor node in the marine IoT service based on low power wide area (LPWA) network. The proposed antenna is designed for multi-band in ISM band. The specification of the proposed antenna expressed here is also for multi-band service.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.163-167
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• 2006

Multi-level ${\mu}TESLA$ is an efficient extension to ${\mu}TESLA$ to provide an extended lifetime for long-lived sensor networks. This paper presents new constructions of multi-level ${\mu}TESLA$ with immediate authentication of key chain commitments. The proposed constructions are shown to be more efficient and robust than the previous multi-level ${\mu}TESLA$ extension.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.47-56
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• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.