Cherntanomwong, Panarat

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Cherntanomwong, Panarat
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panarat.ch@kmitl.ac.th

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    Location fingerprint technique using Fuzzy C-Means clustering algorithm for indoor localization
    (2011-12-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    ;
    Takada, Jun Ichi
    The recent researches in localization technique have been supported by the emerging of wireless sensor network (WSN) technology. The issues of power and time consumption have become the main research topics in WSN-based localization technique. ZigBee as IEEE 802.15.4 is commonly used as supporting device because of its advantages for low-power, small and smart sensor nodes. This paper proposes the new technique in radio frequency (RF) fingerprint technique-based localization using Fuzzy C-Means (FCM) clustering algorithm. This technique provides an efficient localization system that gives benefit in the time-efficient and low power consumption. In this paper, received signal strength indicator (RSSI) is used as the fingerprint information which indicates the location of sensor nodes. The different amount of the reference nodes is applied. The effectiveness of this method is verified by an indoor experiment. The estimated location results from different sets of reference nodes are compared. The time consumption in experiment is compared with those using the common fingerprint technique. © 2011 IEEE.
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    Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm
    (2011-12-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    ;
    Takada, Jun Ichi
    ZigBee as IEEE 802.15.4 standard has been using for main research topic in the wireless sensor network (WSN) applications. The new method of radio frequency (RF) fingerprint-based technique for indoor localization is proposed. The received signal strength indicator (RSSI) is used as database values which correspond to the location of the sensor nodes. Fuzzy C-Means (FCM) clustering algorithm is applied as the experiment data cluster method. FCM algorithm is deployed to cluster the obtained feature vectors into several classes corresponding to the different amount of RSSI values. The results show that FCM can cluster the target node in a group of the fingerprint database. The location of target node is arranged in various forms to validate the accuracy of the clustering technique. Euclidean distance is used as the parameter to compare the similarity between fingerprint database and the target location. The results show that the new method is simple and effective method to reduce the complexity and to support the low power and to reduce the time using in the fingerprint-based localization technique. © 2011 IEEE.
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    Robustness of 3D indoor localization based on fingerprint technique in wireless sensor networks
    (2013-09-02)
    Chuenurajit, Thanapong
    ;
    Phimmasean, Sisongkham
    ;
    Cherntanomwong, Panarat
    The simple approaches of an indoor localization have been continuously developed and extensively published. In order to achieve a challenge of the effective indoor localization, the expected localization system should be a high efficiency such as high accuracy, simple, robustness and effective system. For the indoor localization, the researches have been supported by the advancement of the sensor node technology. One application of this technology is wireless sensor networks-based indoor localization. The simple approaches of 2-dimensional (2D) indoor localization have been widely proposed. Since a realistic system includes complicated terrain and different environment, 3-dimensional (3D) consideration is more suitable to be applied in the real life. This paper proposes an approach of 3D indoor localization based on ZigBee standard. Fingerprint technique-based received signal strength indicator (RSSI) is employed. Due to fluctuating signals in indoor environment, robustness of fingerprint technique will be proposed in order to solve the propagation mechanisms. The k-Nearest Neighbor using Euclidean distance is utilized as the pattern matching algorithm. For the case study, 8 reference nodes and 1 target node are stationary placed on a bookshelf in clean and human body's effect environments. The expected errors of estimated target locations should not be more than 36 cm (each level height). From the results, we can acquire an acceptable accuracy. It shows that our system can be applied in the real application. © 2013 IEEE.