Radio frequency identification (RFID) system has gained attention as a new identification source that achieves a ubiquitous environment. Each RFID tag has a unique ID and is attached to an object. A user reads the unique ID of an RFID tag by using RFID readers and obtains the information on the object. One of the important technologies that use the RFID systems is the position estimation of RFID tags. Position estimation means estimating the location of the object with the RFID tag. Acquiring the location information of the RFID tag can be very useful. If a user can know the position of the RFID tag, the position estimation can be applied to a navigation system for walkers. In this paper, we propose a new position estimation method named Swift Communication Range Recognition (S-CRR) as an extended improvement on previous CRR that shortens the estimation delay. In this method, the position of an RFID tag is estimated by selecting the communication area model that corresponds to its boundary angles. We evaluated its performance by experiments and simulations of the RFID system. As the results, we found that S-CRR can estimate the position of an RFID tag comparatively accurately and quickly.

The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

Recently, the border security systems attract attention as large-scale monitoring system in wireless sensor networks (WSNs). In the border security systems whose aim is the monitoring of illegal immigrants and the information management in long-period, it deploys a lot of sensor nodes that have the communication and sensing functions in the detection area. Hence, the border security systems are necessary to reduce the power consumption of the whole system in order to extend the system lifetime and accurately monitor the track of illegal immigrants. In this paper, we propose two effective barrier coverage construction methods by switch dynamically operation modes of sensor nodes to reduce the operating time of the sensing function that wastes a lot of power consumption. We carry out performance evaluations by computer simulations to show the effectiveness of two proposed methods and show that the proposed methods are suitable for the border security systems.

The radio frequency identification (RFID) system has attracting attention as a new identification source that achieves a ubiquitous environment. Each RFID tag has a unique ID code, and is attached on an object whose information it contains. A user reads the unique ID code using RFID readers and obtains information about the object. One of the important applications of RFID technology is the indoor position estimation of RFID tags. It can be applied to navigation systems for people in complex buildings. In this paper, we propose an effective position estimation method named Broad-type Multi-Sensing-Range (B-MSR) method to improve the estimation error of the conventional methods using sensor model. A new reader antenna with two flexible antenna elements is introduced into B-MSR. The distance between two flexible antenna elements can be adjusted. Thus, two kinds of system parameters can be controlled, the distance between two antenna elements and the transmission power of the RFID reader. In this paper, four sensing ranges are settled by controlling the values of two parameters. The performance evaluation shows four characteristics of B-MSR. Firstly, it reduces the initial estimation error. Secondly, it reduces the moving distance. Thirdly, it reduces the number of different sensing points. Fourthly, it shortens the required estimation time.