• Journal of Computing Science and Engineering
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• v.5 no.1
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• pp.71-84
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• 2011

Pervasive applications such as digital memories or patient monitors collect a vast amount of data. One key challenge in these systems is how to extract interesting or unusual information. Because users cannot anticipate their future interests in the data when the data is stored, it is hard to provide appropriate indexes. As location-tracking technologies, such as global positioning system, have become ubiquitous, digital cameras or other pervasive systems record location information along with the data. In this paper, we present an automatic approach to identify unusual data using location information. Given the location information, our system identifies unusual days, that is, days with unusual mobility patterns. We evaluated our detection system using a real wireless trace, collected at wireless access points, and demonstrated its capabilities. Using our system, we were able to identify days when mobility patterns changed and differentiate days when a user followed a regular pattern from the rest. We also discovered general mobility characteristics. For example, most users had one or more repeating mobility patterns, and repeating mobility patterns did not depend on certain days of the week, except that weekends were different from weekdays.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.31-37
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• 2011

Recently, MANET(Mobile Ad-hoc Network) is developing increasingly in the wireless network. MANET has weakness because phases change frequently and MANET doesn't have middle management system. Every node which consists of MANET has to perform data forwarding, but traceback is not reliable if these nodes do malicious action owing to attack. It also is not easy to find location of attacker when it is attacked as moving of nodes. In this paper, we propose a hierarchical-based traceback method that reduce waste of memory and can manage path information efficiently. In order to manage trace path information and reduce using resource in the cluster head after network is formed to cluster, method which recomposes the path efficiently is proposed. Proposed method in this paper can reduce path trace failure rate remarkably due to moving of nodes. It can also reduce the cost for traceback and time it takes to collect information.

• ETRI Journal
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• v.28 no.4
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• pp.537-540
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• 2006

When multiple ZigBee wireless personal area networks (WPANs) are in close proximity to each other, contentions and collisions in transmissions will lead to increased packet delays. However, there is no existing study on how delay performance would be affected in a crowded real-life environment where each person walking down a busy street would be wearing a ZigBee WPAN. This letter studies the use of ZigBee WPANs in such a real-life environment for real-time heart beat monitoring. To be pragmatic, we derived a mobility pattern from the analysis of a real-life video trace. Then, we estimated the delay performance from the video trace by combining data collected from ZigBee experiments. The results show that the 300 ms packet delay requirement will not be met for only 11% of the time. When failure occurs, it will last for an average duration of 1.4 s.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.357-365
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• 2009

To effectively exploit the underlying network bandwidth while maximizing user perceivable QoS, mandatory to make proper estimation on packet loss and queuing delay of the underling network. This issue is further emphasized in wireless network environment where network bandwidth is scarce resource. In this work, we focus our effort on developing performance model for wireless network. We collect packet trace from actually wireless network environment. We find that packet count process and bandwidth process in wireless environment exhibits long range property. We extract key performance parameters of the underlying network traffic. We develop an analytical model for buffer overflow probability and waiting time. We obtain the tail probability of the queueing system using Fractional Brown Motion (FBM). We represent average queuing delay from queue length model. Through our study based upon empirical data, it is found that our performance model well represent the physical characteristics of the IEEE 802.11b network traffic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.351-356
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• 2011

In this paper, we design a pet location tracking and remote monitoring system that uses ultrasonic, temperature, humidity and illumination sensors to study behavioral patterns and habits. Using ultrasonic waves to calculate distances, a WSN(Wireless Sensor Network) was constructed to transmit data at pet's location, such as temperature, humidity and illumination, to a sink mote. Data received by the system are stored in the database in real time to trace pet's location. Interference among transmitting motes was eliminated by sequentially transmitting RF beacons using sink mote's beacon as the reference signal. Experiments were performed with the laboratory prototype of a pet animal monitoring system implemented for this study. The system analyzes locations of a pet and displays movement patterns, areas of movement, temperature, humidity and illumination using a GUI (graphical user interface).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1B
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• pp.52-63
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• 2004

Packet loss process of cdma2000 mobile Internet channel deployed in Korea is modeled as a two state Markov process known as Gilbert model. This paper proposes the procedures to derive four parameters of the our modified Gilbert model from packet loss trace taken from two major cdma2000 networks in Korea. These four parameters are derived in various situations, that is, with fixed and moving terminals, in open field and urban areas. They can be used to produce synthetic packet loss patterns for study of the channel. Moreover, if they are calculated on-line during multimedia service, they can be used to make loss protection controls adaptive to network condition.

• International Journal of High-Rise Buildings
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• v.3 no.2
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• pp.155-165
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• 2014

This study measured and compared the variation of ventilation rate and fan energy consumption according to various control strategies after installing wireless sensor-based pilot ventilation system in order to verify the applicability of demand-controlled ventilation (DCV) strategy that was efficient ventilation control strategy for underground parking lot. The underground parking lot pilot ventilation system controlled the ventilation rate by directly or indirectly tracking the traffic load in real-time after sensing data, using vehicle detection sensors and carbon monoxide (CO) and carbon dioxide ($CO_2$) sensor. The ventilation system has operated for 9 hours per a day. It responded real-time data every 10 minutes, providing ventilation rate in conformance with the input traffic load or contaminant level at that time. A ventilation rate of pilot ventilation system can be controlled at 8 levels. The reason is that a ventilation unit consists of 8 high-speed nozzle jet fans. This study proposed vehicle detection sensor based demand-controlled ventilation (VDS-DCV) strategy that would accurately trace direct traffic load and CO sensor based demand-controlled ventilation (CO-DCV) strategy that would indirectly estimate traffic load through the concentration of contaminants. In order to apply DCV strategy based on real-time traffic load, the minimum required ventilation rate per a single vehicle was applied. It was derived through the design ventilation rate and total parking capacity in the underground parking lot. This is because current ventilation standard established per unit floor area or unit volume of the space made it difficult to apply DCV strategy according to the real-time variation of traffic load. According to the results in this study, two DCV strategies in the underground parking lot are considered to be a good alternative approach that satisfies both energy saving and healthy indoor environment in comparison with the conventional control strategies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.189-198
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• 2008

At present, companies write daily work record or use bar-code in order to collect distribution flow data in real time. However, it needs additional works to check the record or read the bar-code with a scanner. In this case, human error could decrease accuracy of data and it would cause problems in reliability. To solve this problem, RFID (Radio Frequency Identification) is introduced in many automatic recognition sector recently. RFID is a technology that identification data is inserted into micro-mini IC chip and recognize, trace, and manage object, animal, or person using wireless frequency. This is being emerged as the core technology in future ubiquitous environment. This study is intended to suggest RFID application method in franchise business. Traceability and visibility of individual product are supplied based on EPCglobal network. It includes DW system which supplies various assessment data about product in supply chain, financial transaction system which is based on product transaction and position information, and RFID middleware which refines and divides product data from RFID tag. With the suggested application methods, individual product's profile data are supplied in real time and it would boost reliability to customer and make effective cooperation with existing operation systems (SCM, CRM, and e-Business) possible.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.203-210
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• 2011

The current sensor network analyzes the data collected by the sensing of fixed sensor nodes and provides a service. However, this method cannot actively handle the state and the change in the position of people, 'the target for sensing and the change in the environment', including home automation, building automation and real-time road & weather information, and healthcare environment, etc. To support a dynamic situation which is appropriate for an individual in this diverse environment, it is necessary to provide actively differentiated specific information according to the movement of people and the changes in the environment. In this study, a individualized sensor mobile agent middleware which provides the individualized information (the location of fire incidence and the trace for the path of spread), has been realized through the sensor network environment constructed by the installation of wireless sensor nodes mounted with mobile agent middlewares in buildings.