• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1357-1368
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• 2017

Sensor networks are used in various fields such as marine, defense, and smart home etc. Among the components of the sensor network, the sensor node collects sensor data, as one of the representative sensor network roles, and the sensor node makes a greate influence on the overall performance of the sensor network. Therefore, how to design the sensor node is an important issue in the sensor network field. However, the research on the sensor network architecture suitable for the sensor network installation environment has been made more important than the research on how to configure the sensor node. In this paper, we propose to identify elements to be considered for designing a sensor node that makes a large influence on the performance of the sensor network, and to easily implement the sensor node through the state transition model based on these elements.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.396-402
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• 2010

The tracking of current location of residents is an essential requirement for context-aware service of smart houses. This paper presents a wireless sensor network system which could detect location transition such as entrance and exit to a room and also identify the user who passed the room, without duty of wearing any sort of tag. We designed new sensor node to solve the problem of short operation lifetime of previous work[1] which has two pyroelectric infrared (PIR) sensors and an ultrasonic sensor, as well as a 2.4 GHz radio frequency wireless transceiver. The proposed user identification method is to discriminate a person based on his/her height by using an ultrasonic sensor. The detection idea of entering/exiting behavior is based on order of triggering of two PIR sensors. The topology of the developed wireless sensor network system is simple star structure in which each sensor node is connected to one sink node directly. We evaluated the proposed sensing system with a set of experiments for three subjects in a model house. The experimental result shows that the averaged recognition rate of user identification is 81.3% for three persons. and perfect entering/exiting behavior detection performance.