• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.896-898
• /
• 2012

Recently, many countries are making efforts for the development of ocean resources because the necessity and importance of the ocean resources are increased. Underwater sensor networks have emerged as a very powerful technique for many applications, including monitoring, measurement, surveillance and control and envisioned to enable applications for oceanographic data collection, ocean sampling, environmental and pollution monitoring, offshore exploration, disaster prevention, tsunami and seaquake warning, assisted navigation, distributed tactical surveillance, and mine reconnaissance. The idea of applying sensor networks into underwater environments (i.e., forming underwater sensor networks) has received increasing interests in monitoring aquatic environments for scientific, environmental, commercial, safety, and military reasons. The data obtained by observing around the environment are wireless-transmitted by a radio set with various waves. According to the technical development of the medium set, some parameters restricted in observing the ocean have been gradually developed with the solution of power, distance, and corrosion and watertight by the seawater. The actual matters such as variety of required data, real-time observation, and data transmission, however, have not enough been improved just as various telecommunication systems on the land. In this paper, a wireless management system will be studied through a setup of wireless network available at fishery around the coast, real-time environmental observation with RF sensor, and data collection by a sensing device at the coastal areas.

• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.43-48
• /
• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.2
• /
• pp.1-9
• /
• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.152-161
• /
• 2014

In this paper, a multi-node real-time diagnostic and management system based on zigbee sensor network is proposed, which is to monitor and diagnose multiple nodes as well as to control the data generated from the various multiple sensors collectively. The proposed system is designed to transmit the collected wireless and wired data to the server for monitoring and controling efficiently the condition for multi-nodes by taking the corresponding actions according to the analysis. The system is implemented to make it possible to manage the sensor data by classifying them, of which data are issued from the clustered sources with a number of the remote sensors. In order to evaluate the performance of the proposed system, we measure and analyze both the transmission delay time according to the distance and the data loss rate issued from multiple sensors. The results shows that the proposed system has a good performance.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.763-764
• /
• 2006

The rapid growth of Hardware technologies and Network fitted to us Ubiquitous Computing Life[1]. Ubiquitous Computing integrates computation into the environment, rather than having computers which are distinct objects. There are many computational things like a Sensor Network, RFID, GPS, Mobile devices, and so on. Ubiquitous Data Integration and Management are new paradigms. The goals of UDI Service Platform are data protection for the distributed data on pervasive computing devices and data distribution to appropriate users with best distribution policies. To implement the idea we evaluate the logical schema Cyber View that is a management tool.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.10
• /
• pp.69-74
• /
• 2014

With the increasing interest of energy efficiency, several buildings and factories begin to monitor energy usages with a built-in energy management system. However, the built-in energy monitoring system does not reflect the dynamics of buildings and factories energy usage. To overcome the latter, we deploy several sensors to monitor the dynamics of buildings energy usage patterns. In this paper, we are proposing a framework of a sensor based energy monitoring system. Based on our limited experiments, we can monitor power usages by a person, device and time period. As a result, we can plan a better energy usage and improve energy efficiency by the monitored energy usage profile data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.11
• /
• pp.1946-1958
• /
• 2011

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.3
• /
• pp.943-961
• /
• 2020

With the advent of IoT technology and Big Data computing, the importance of WSNs (Wireless Sensor Networks) has been on the rise. For energy-efficient and collection-efficient delivery of any sensed data, lots of novel wireless medium access control (MAC) protocols have been proposed and these MAC schemes are the basis of many IoT systems that leads the upcoming fourth industrial revolution. WSNs play a very important role in collecting Big Data from various IoT sensors. Also, due to the limited amount of battery driving the sensors, energy-saving MAC technologies have been recently studied. In addition, as new IoT technologies for Big Data computing emerge to meet different needs, both sensors and sinks need to be mobile. To guarantee stability of WSNs with dynamic topologies as well as frequent physical changes, the existing MAC schemes must be tuned for better adapting to the new WSN environment which includes energy-efficiency and collection-efficiency of sensors, coverage of WSNs and data collecting methods of sinks. To address these issues, in this paper, a self-organization scheme for mobile sensor networks with mobile multiple sinks has been proposed and verified to adapt both mobile sensors and multiple sinks to 3-dimensional group management MAC protocol. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of the various usage cases. Therefore, the proposed self-organization scheme might be adaptable for various computing and networking environments with big data.

• Journal of Korea Spatial Information System Society
• /
• v.12 no.1
• /
• pp.18-27
• /
• 2010

In u-GIS environment, GeoSensor environment requires that dynamic data captured from various sensors and static information in terms of features in 2D or 3D are fused together. GeoSensors, the core of this environment, are distributed over a wide area sporadically, and are collected in any size constantly. As a result, storage space could be exceeded because of restricted memory in DSMS. To solve this kind of problems, a lot of related studies are being researched actively. There are typically 3 different methods - Random Load Shedding, Semantic Load Shedding, and Sampling. Random Load Shedding chooses and deletes data in random. Semantic Load Shedding prioritizes data, then deletes it first which has lower priority. Sampling uses statistical operation, computes sampling rate, and sheds load. However, they are not high accuracy because traditional ones do not consider spatial characteristics. In this paper 'Pre-Filtering based Post Load Shedding' are suggested to improve the accuracy of spatial query and to restrict load shedding in DSMS. This method, at first, limits unnecessarily increased loads in stream queue with 'Pre-Filtering'. And then, it processes 'Post-Load Shedding', considering data and spatial status to guarantee the accuracy of result. The suggested method effectively reduces the number of the performance of load shedding, and improves the accuracy of spatial query.

• Journal of Korea Multimedia Society
• /
• v.12 no.12
• /
• pp.1778-1786
• /
• 2009

Many sensor network applications have been developed for smart home, disaster management, and a wide range of other applications. These applications, however, generally assume a fixed base station as well as fixed sensor nodes. Previous research on sensor networks mainly focused on efficient transmission of data from sensors to fixed sink nodes. Recently there has been active research on mobile sink nodes, sink mobility is one of the most comprehensive trends for information gathering in sensor networks, but the research of an environment where both fixed sink nodes and mobile sinks are present at the same time is rather scarce. This paper proposes a scheme for context-aware by ubiquitous devices with the sink functionality added through fixed sinks under a previously-built, cluster-based multi-hop sensor network environment. To this end, clustering of mobile devices were done based on the fixed sinks of a previously-built sensor network, and by using appropriate fixed sinks, context gathering was made possible. By mathematical comparison with TTDD routing protocol, which was proposed for mobile sinks, it was confirmed that performance increases by average 50% in energy with the number of mobile sinks, and with the number of movements by mobile devices.