• International Journal of Advanced Culture Technology
• /
• v.5 no.2
• /
• pp.98-105
• /
• 2017

Smartphones are fast growing in the IT market and are the most influential devices in our daily life. Smartphones are being studied for their use in body sensor networks with excellent processing power and wireless communication technology. In this paper, we propose a coordinator design that provides data collection, classification, and display using based on Android-smartphone in multiple sensor nodes. The coordinator collects data of sensor nodes that measure biological patterns using wireless communication technologies such as Bluetooth and NFC. The coordinator constructs a network using a multiple-level scheduling algorithm for efficient data collection at multiple sensor nodes. Also, to support different protocols between heterogeneous sensors, a data sheet recording wireless communication protocol information is used. The designed coordinator used Arduino to test the performance of multiple sensor node environments.

• ETRI Journal
• /
• v.42 no.3
• /
• pp.341-350
• /
• 2020

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

• Journal of Computing Science and Engineering
• /
• v.5 no.1
• /
• pp.71-84
• /
• 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 the Korea Safety Management & Science
• /
• v.2 no.3
• /
• pp.129-140
• /
• 2000

Nowadays the role of information systems is getting more and more increased according to the development of information technology. In order to manage complex, various and huge data, it is vital to construct efficient information system. For this effective information system, data have properly to be stored, encoded and represented when needed. This paper presents equipment management information system using wireless application protocol. This system enables us to have remote control of data searching and data management. Also, through the technique of data mining, database resulted from this system can be utilized into expectation and analysis about life-cycle, characteristic, and failure time of equipment, pattern recognition of users, and state of movement, etc.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.2 no.3
• /
• pp.48-60
• /
• 2003

With the rapid successful deployment of WLANs worldwide in numerous hotspots for high data rate, wireless access for data services has created the need to integrate the Cellular Networks with WLAN Networks. The integrated wireless environment will provide the end user a much better service experience along with a single subscription and a single bill. In this paper we focus on inter-working approaches, which combine WLANs and Cellular Networks into an integrated wireless environment capable of ubiquitous access to data services and very high data rates in hotspots areas. We first list the key requirements which are currently being standardized within the 30PP for integration with WLANs networks. We discuss two inter-working architectures namely loosely coupled and tightly coupled This paper will detail the loosely coupled inter-working approach while briefly discussing the tightly coupled inter-working. Finally, we will conclude that the loosely coupled approach is evolutionary and less intrusive than the tightly coupled approach.

• Proceedings of the KSR Conference
• /
• 2000.05a
• /
• pp.180-187
• /
• 2000

This paper presents the system of analyzing data in memory of TCMS. This system can show physical data in memory to text and graphic : format. To transfer data from TCMS to this system, a large number of system have used to memory IC card. the method of using memory IC card as a intermediation has many points at issue, that is a speed of transmitting data, life time of IC card and identity of train system each other. So this paper proposes that the method of wireless LAM be adopted by this system to improve the week point of previous method and other method to better the method of wireless LAN.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.2
• /
• pp.126-131
• /
• 2008

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. It has new information collection scheme and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the limited resources and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faulty sensors and take necessary actions for the reconstruction of the lost sensor data caused by fault as earlier as possible. In this paper, we propose an recursive PCA-based fault detection and lost data reconstruction algorithm for sensor networks. Also, the performance of proposed scheme was verified with simulation studies.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.83-95
• /
• 2020

Because sensor nodes have limited resources in wireless sensor networks, data aggregation can efficiently reduce communication overhead and extend the network lifetime. Although many existing methods are particularly useful for data aggregation applications, they incur unbalanced communication cost and waste lots of sensors' energy. In this paper, we propose a privacy-preserving, energy-saving data aggregation scheme (EBPP). Our method can efficiently reduce the communication cost and provide privacy preservation to protect useful information. Meanwhile, the balanced energy of the nodes can extend the network lifetime in our scheme. Through many simulation experiments, we use several performance criteria to evaluate the method. According to the simulation and analysis results, this method can more effectively balance energy dissipation and provide privacy preservation compared to the existing schemes.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.212-213
• /
• 2015

Data aggregation is a fundamental problem in wireless sensor networks that has attracted great attention in recent years. On constructing a robust algorithm for minimizing data aggregation delay in wireless sensor networks, we consider limited transmission range sensors and approximate the minimum-delay data aggregation tree which can only be built in networks of unlimited transmission range sensors. The paper proposes an adaptive method that can be applied to maintain the network structure in case of a sensor node fails. The data aggregation tree built by the proposed scheme is therefore self-healing and robust. Intensive simulations are carried out and the results show that the scheme could adapt well to network topology changes compared with other approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2488-2511
• /
• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.