• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.89-91
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• 2022

Wireless sensor networks(WSN), which are mainly used for indoor positioning, rely on the accuracy of RSSI. This RSSI is sensitive to several factors that cause interference, and there are foreign studies showing that temperature has a large effect on RSSI in indoor/outdoor environment among several factors. The temperature of the indoor space is uneven due to heat sources or air cooling systems indoors, and temperature changes frequently occur at certain locations. In particular, in case of an indoor fire, the accurate positioning system is required to guide an evacuation route, but a high temperature is locally formed due to a fire around the receiver, so the RSSI value could be influenced. In this paper, the effect on RSSI is studied by analyzing the correlation between SoC internal/local temperature and RSSI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1087-1096
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• 2011

Vehicular Ad Hoc Network(VANET) using wireless network is offering the communications between vehicle and vehicle(V2V) or vehicle and infrastructure(V2I). VANET is being actively researched from industry field and university because of the rapid developments of the industry and vehicular automation. Information, collected from VANET, of velocity, acceleration, condition of road and environments provides various services related with safe drive to the drivers, so security over network is the inevitable factor. For the secure message authentication, a number of authentication proposals have been proposed. Among of them, a scheme, proposed by Jung, applying database search algorithm, Bloom filter, to RAISE scheme, is efficient authentication algorithm in a dense space. However, k-anonymity used for obtaining the accurate vehicular identification in the paper has a weak point. Whenever requesting the righteous identification, all hash value of messages are calculated. For this reason, as the number of car increases, a amount of hash operation increases exponentially. Moreover the paper does not provide a complete key exchange algorithm while the hand-over operation. In this paper, we use a Received Signal Strength Indicator(RSSI) based velocity and distance estimation algorithm to localize the identification and provide the secure and efficient algorithm in which the problem of hand-over algorithm is corrected.