• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.2
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• pp.71-78
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• 2012

Based on the Zigbee-based wireless sensor network, I suggest the way to reduce errors between the short distance, improving the accuracy of the presumed distance by revising the deviation of RSSI(Received Signal Strength Indication) values is to estimate the distance using only the RF signal power without the additional hardware. In general, the graph measured by RSSI values shows the proximity values which are ideally reduced in proportion to the distance under the free outdoor space in which LOS(Line-Of-Sight) is guaranteed. However, if the result of the received RSSI values are each substituted to the formula, it can produce a larger margin of error and less accurate measurement since it is based upon the premise that this free space is not affected by reflected waves or obstacles caused by the ground and electronic jamming engendered by the environment. Therefore, the purpose of this study is to reduce the margin of errors between the distances and to measure the proximity values with the ideal type of graph by suggesting the way to revise the received RSSI values in the light of these reflected waves or obstacles and the electronic jamming. In conclusion, this study proves that errors are reduced by comparing the proposed deviation correction method to the revised RSSI value.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.63-68
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• 2017

In this paper, Distance recognition measurement using distance calculation correction algorithm, was realization through LED dimming control. The calculation values for the RSSI average filtering and the RSSI feedback filtering were calculated and applied to reduce the error of the RSSI value measured from a long distance. It was confirmed that the RSSI values through the average filtering and the RSSI values measured by setting the coefficient value of the feedback filtering to 0.5 were ranged from -61 dBm to - 52.5 dBm, which shows irregular and high values decrease slightly as much as about -2 dBm to -6 dBm as compared to general measurements. A distance calculation correction algorithm to improve the accuracy was applied, which confirmed that as the distance increases, the range of errors decreases. In conclusion, unstable signals were corrected using the RSSI measurement result filtering, and the distance calculation correction algorithm was applied and performed to reduce the range of errors. In addition, RGB colors were implemented by LED to indicate the distance determination and the signal stability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2172-2178
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• 2007

In indoor environment, the combination of the two variations, large scale(path loss) and small scale(fading), leads to non-linear variation of RSSI(received signal strength indicator) values as distance varied. This has been one of the difficulties for indoor location estimation. This paper presents new findings on indoor RSSI characterization for more accurate model building. Experiments have been done statistically to find overall trend of RSSI values at different places and times within the same room. From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. These two factors are directly indicated by the two main parameters of path loss model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. The temporal parameter also has a large scale variation effect that is slowly time varying due to environmental changes. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.25-31
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• 2017

The indoor location based service proposed in the study was assigned to target a moving user. Positioning in the outdoor environment is accurate while using GPS. However, in an indoor environment, positioning is inaccurate and difficult. In order to overcome this, studies of various techniques for positioning based on wireless communication such as Wi-Fi, Zigbee and Bluetooth are being performed. The RSSI value and the delivery signal of the bluetooth beacon are measured according to the distance, and to a database. It was applied calculating the value for the average RSSI and the RSSI filtering feedback. Filtering is used to reduce the error of the RSSI values that are measured at long distance. When average and feedback filtering coefficient are set with 0.5, irregular and highly RSSI values are decreased. As the distance increases, the range of error is confirmed to have a reduction when using a distance calculation correction algorithm. Finally, when using the RSSI measurement results filtering, it corrects an unstable signal. Also, the distance correction algorithm is used to reduce a range of errors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.85-90
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• 2017

Indoor location-based services have been developed based on the Internet of Things technologies which measure and analyze users who are moving in their daily lives. These various indoor positioning technologies require separate hardware and have several disadvantages, such as a communication protocol which becomes complicated. Based on the fact that a reduction in signal strength occurs according to the distance due to the physical characteristics of the transmitted signal, RSSI technology that uses the received signal strength of the wireless signal used in this paper measures the strength of the transmitted signal and the intensity of the attenuated received signal and then calculates the distance between a transmitter and a receiver, which requires no separate costs and makes to implement simple measurements. It was applied calculating the value for the average RSSI and the RSSI filtering feedback. Filtering is used to reduce the error of the RSSI values that are measured at long distance.It was confirmed that the RSSI values through the average filtering and the RSSI values measured by setting the coefficient value of the feedback filtering to 0.5 were ranged from -61 dBm to - 52.5 dBm, which shows irregular and high values decrease slightly as much as about -2 dBm to -6 dBm as compared to general measurements.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.78-87
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• 2008

Received signal strength indicator (RSSI) is used as one of the ranging techniques to locate dynamic sensor nodes in wireless sensor network. Before it can be used for position estimation, RSSI values must be converted to distances using path loss model. These distances among sensor nodes are combined using trilateration method to find position. This paper presents an idea which attempts to integrate both path loss model and trilateration as one algorithm without going through RSSI-distance conversion. This means it is not simply formulas combination but a whole new model was developed. Several advantages were found after integration: it is able to reduce processing load, and ensure that all values do not exceed the maximum range of 16-bit signed or unsigned numbers due to antilog operation in path loss model. The results also show that this method is able to reduce estimation error while inaccurate environmental parameters are used for RSSI-distance conversion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.742-745
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• 2009

This paper proposed indoor location recognition method based on RSSI (received signal strength indication) using the LVQ (Learning Vector Quantization) network. The LVQ inputs are the RSSI values measured by the fixed reference nodes and the output are the spatial sections. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.717-720
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• 2020

An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.23-30
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• 2016

In this paper, for the pratical building envoronments, the propagation properties of the electromagnetic waves of the sub-1GHz bands, including the 447MHz, 868MHz, and 715MHz, and the 2.4GHz band are experimentally observed in therms of the received signal strength indicator (RSSI) value. The compasion of the frequency bands can be utilized to efficiently construct the wireless sensor networks (WSN) for the building automation control. In order to measure the RSSI values in the building, an RSSI measurement system is first designed, in which the master part can transmit data packets and measure the corresponding RSSI values, and the slave part can respond the received data packets. Using the measurement system, the RSSI values are then experimentally measured at four types of building enviroments. From the experimental result analysis, we could notice that the sub-1GHz, especially the 447MHz band, showd a good communication performance for the building environment and could provide an efficient WSN construction when the data rate is relatively low.