• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1496-1501
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• 2013

IEEE 802.11 has become the main technology in wireless network. However, performance anomalies, especially in terms of fairness, arise in its use in ad hoc networks. Two independent emitters nodes can easily monopolize the medium, preventing other nodes to send packets. This paper proposes the modified (m,k)-firm scheme which is based on T-MAC to improve fairness. The basic idea is to assign higher priorities to nodes that are closer to a dynamic failure so as to improve their chances of meeting their deadlines. The simulation result shows that the proposed scheduling scheme helps improve the fairness of the DCF.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.571-573
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• 2018

The loss of data collected from the sensor node in the wireless sensor network environment is caused by the hidden node of the sensor node and power shortage. In order to solve these problems, researches have been actively carried out to maintain the network effectively, but there is no study on the situation where the maintenance of the network is impossible. Therefore, research is needed to infer lost data in situations where network maintenance is impossible. In this paper, use particulate matter data of specific cities to deduce lost data. Analyze the accumulated data through machine learning and identify the possibility of inferring lost data.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.185-194
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• 2006

In this paper, we first propose a power-and-bandwidth efficient cooperative transmission protocol where a sensor node assists two others for their data transmission to a clusterhead in WSNs (Wireless Sensor Networks) using LEACH (Low-Energy Adaptive Clustering Hierarchy). Then we derive its closed-form BER expression which Is also a general BER one for the decode-and-forward protocol (DF) and Prove that the proposed protocol performs as same as the conventional DF but obtains higher spectral efficiency. A variety of numerical results reveal the cooperation can save the network power up to 11dB over direct transmission at BER of $10^{-3}$.

• Journal of Communications and Networks
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• v.11 no.1
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• pp.57-71
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• 2009

Networking together hundreds or thousands of cheap sensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. As sensor nodes are typically battery operated, it is important to efficiently use the limited energy of the nodes to extend the lifetime of the wireless sensor network (WSN). One of the fundamental issues in WSNs is the coverage problem. In this paper, the border coverage problem in WSNs is rigorously analyzed. Most existing results related to the coverage problem in wireless sensor networks focused on planar networks; however, three dimensional (3D) modeling of the sensor network would reflect more accurately real-life situations. Unlike previous works in this area, we provide distributed algorithms that allow the selection and activation of an optimal border cover for both 2D and 3D regions of interest. We also provide self-healing algorithms as an optimization to our border coverage algorithms which allow the sensor network to adaptively reconfigure and repair itself in order to improve its own performance. Border coverage is crucial for optimizing sensor placement for intrusion detection and a number of other practical applications.

• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.185-192
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• 2010

In sensor network environments, each sensor measures the physical environments according to the sampling period, and transmits a sensor reading to the base station. Thus, the sample period influences against importance resources such as a network bandwidth, and a battery power. In this paper, we propose new adaptive sampling technique that adjusts the sampling period of a sensor with respect to the features of sensor readings. The proposed technique predicts a future readings based on KF (Kalman Filter). By using the differences of actual readings and estimated reading, we identify the importance of sensor readings, and then, we adjust the sampling period according to the importance. In our experiments, we demonstrate the effectiveness of our technique.

• Proceedings of the KSRS Conference
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• v.2
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• pp.768-771
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• 2006

In WSN(Wireless Sensor Network) environment, a large amount of sensors, which are small and heterogeneous, generates data stream successively in physical space. These sensors are composed of measured data and metadata. Metadata includes various features such as location, sampling time, measurement unit, and their types. Until now, wireless sensors have been managed with individual specification, not the explicit standardization of metadata, so it is difficult to collect and communicate between heterogeneous sensors. To solve this problem, OGC(Open Geospatial Consortium) has proposed a SensorML(Sensor Model Language) which can manage metadata of heterogeneous sensors with unique format. In this paper, we introduce a metadata model using SensorML specification to manage various sensors, which are distributed in a wide scope. In addition, we implement the metadata management module applied to the sensor data stream management system. We provide many functions, namely generating metadata file, registering and storing them according to definition of SensorML.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1159-1166
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• 2010

Today, detecting the location of moving object has been traced as various methods in our world. In this paper, we preset the system to improve the estimation accuracy utilizing detail localization using radar sensor based on WSN and situational awareness for a calibration (context aware) database, Rail concept. A variety of existing location tracking method has a problem with receiving of data and accuracy as tracking methodology, and since these located data are the only data to be collected for location tracing, the context aware or monitering as the surrounding environment is limited. So, in this paper, we enhanced the distance aware accuracy using radar sensor utilizing the Doppler effect among the distance measuring method, estimated the location using the Triangulation algorithm. Also, since we composed the environment data(temperature, illuminancem, humidity, noise) to entry of the database, it can be utilized in location-based service according to the later action information inference and positive context decision. In order to verify the validity of the suggested method, we give a few random situation and built test bed of designed node, and over the various test we proved the utilizing the context information through route tracking of moving and data processing.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.867-875
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• 2009

A wireless sensor network inherently comprises a plurality of sensor nodes widely deployed for sensing environmental information. To add new functions or to correct some faulty functions of an existing wireless sensor network, the firmware for each sensor node needs to be updated. Firmware update would be quite troublesome if it requires the gathering, reprogramming, and redeploy of all of already deployed sensor nodes. Over-the-air programming (OTA) facilitates the firmware update process, thereby allowing convenient maintenance of an already-deployed sensor network. This paper proposes and implements a remote firmware update mechanism for a TDMA-based wireless sensor network, in which the firmware for sensor nodes constituting the TDMA-based sensor network can be easily updated and the update process can be conveniently monitored from a remote site. We verify the validity of the proposed firmware update method via experiments and introduce three wireless sensor networks installed in outdoor sites in which the proposed firmware update mechanism has been exploited.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.91-98
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• 2016

Outbreaks of highly contagious livestock diseases can cause direct and indirect economic impacts such as lower productivity of cattle farms, fall in tourism in damaged areas and countries, and decline in exports. They also incur tremendous social costs associated with disease elimination and restoration work. Thus, it is essential to prevent livestock diseases through monitoring and prediction efforts. Currently, however, it is still difficult to provide accurate predictive information regarding occurrences of livestock diseases, because existing cattle health monitoring or forecasting systems are only limited to monitor environmental conditions of livestock barns and check activities of cattle by using a pedometer or thermal image. In this paper, we present a biosensor-based cattle health monitoring system capable of collecting bio-signals of farm animals in an effective way. For the presented monitoring system, we design an integrated monitoring device consisting of a sensing module to measure bio-signals of cattle such as the heartbeat, the breath rate and the momentum, as well as a Zigbee module designed to transmit the biometric data based on Wireless Sensor Network (WSN). We verify the validity of the monitoring system by the comparison of the correlations of designed device with a commercial ECG equipment through analyzing the R-peak of measured signals.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.