• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1449-1454
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• 2012

Received signal strength indicator (RSSI) represents the strength of the received signal at the front end of analog-to-digital convertor (ADC) input. RSSI value can be used for deciding the status of channel at the receiver. In this paper, the usage of RSSI in handover is studied using the practical measurement data. We first measure RSSI in 5.9GHz frequency band which is commonly used in wireless access in vehicular environments (WAVE) system. i.e., vehicular communications. Then, to implement a fast handover, the usability of RSSI data is analyzed based on the measured data. We also apply handover in practical highway environments.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.676-682
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• 2011

Sybil attack can disrupt proper operations of wireless sensor network by forging its sensor node to multiple identities. To protect the sensor network from such an attack, a number of countermeasure methods based on RSSI (Received Signal Strength Indicator) and LQI (Link Quality Indicator) have been proposed. However, previous works on the Sybil attack detection do not consider the fact that Sybil nodes can change their RSSI and LQI strength for their malicious purposes. In this paper, we present a Sybil attack detection method based on a transmission power range. Our proposed method initially measures range of RSSI and LQI from sensor nodes, and then set the minimum, maximum and average RSSI and LQI strength value. After initialization, monitoring nodes request that each sensor node transmits data with different transmission power strengths. If the value measured by monitoring node is out of the range in transmission power strengths, the node is considered as a malicious node.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.883-887
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• 2011

Location-based service (LBS) is becoming an important part of the information technology (IT) business. Localization is a core technology for LBS because LBS is based on the position of each device or user. In case of outdoor, GPS - which is used to determine the position of a moving user - is the dominant technology. As satellite signal cannot reach indoor, GPS cannot be used in indoor environment. Therefore, research and study about indoor localization technology, which has the same accuracy as an outdoor GPS, is needed for "seamless LBS". For indoor localization, we consider the IEEE802.11 WLAN environment. Generally, received signal strength indicator (RSSI) is used to obtain a specific position of the user under the WLAN environment. RSSI has a characteristic that is decreased over distance. To use RSSI at indoor localization, a mathematical model of RSSI, which reflects its characteristic, is used. However, this RSSI of the mathematical model is different from a real RSSI, which, in reality, has a sensitive parameter that is much affected by the propagation environment. This difference causes the occurrence of localization error. Thus, it is necessary to set a proper RSSI model in order to obtain an accurate localization result. We propose a method in which the parameters of the propagation environment are determined using only RSSI measurements obtained during localization.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1286-1291
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• 2017

In this paper, we propose an indoor positioning system that makes use of the attenuation model for IEEE 802.11 Channel State Information (CSI) in order to determine its distance from an Access Point (AP) at a fixed position. With the use of CSI, we can mitigate the problems present in the use of Received Signal Strength Indicator (RSSI) data and increase the accuracy of the estimated mobile device's location. For the experiments we performed, we made use of the Intel 5300 Series Network Interface Card (NIC) in order to receive the channel frequency response. The Intel 5300 NIC differs from its counterparts in that it can obtain not only the RSSI but also the CSI between an access point and a mobile device. We can obtain the signal strengths and phases from subcarriers of a system which in turn means making use of this data in the estimation of a mobile device's position.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.56-61
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• 2015

In this paper, the 3D positioning of IoT sensors with the Unity engine of android platform based home-hub was proposde for IoT(Internet of Things) users. Especially, the monitoring of IoT sensor and battery status was designed with the clustering of IoT sensor's position. The 3D positioning of RSSI(received signal strength indicator) and angle for new IoT sensor according to clustering method was described with the cooperation of beacon and received arrival signal time. This unity engine based smart hub platform can monitor the working situation of IoT sensors, and apply 3D video with texture for the life-cycling of many IoT sensors simultaneously. rs was described with RSSI(received signal strength indicator) and received angle.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.67-73
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• 2008

This paper describes a system for location tracking wireless sensor nodes in an indoor environment. The sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface. By tagging users with a mobile node and deploying a number of reference nodes at fixed position in the room, the received signal strength indicator can be used to determine the position of tagged users. The system combines Euclidean distance technique with signal strength obtained by measurement driven log-normal path loss model of 2.4 GHz wireless channel. The experimental results demonstrated the ability of this system to estimate the location with a error less than 1.3m.

• 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.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.494-502
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• 2007

Ubiquitous location based services (u-LBS) will be interested to an important services. They can easily recognize object position at anytime, anywhere. At present, many researchers are making a study of the position recognition and tracking. This paper consists of postion recognition and user identification system. The position recognition is based on location under services (LBS) using a signal strength map, a database is previously made use of empirical measured received signal strength indicator (RSSI). The user identification system automatically controls instruments which is located in home. Moreover users are able to measures body signal freely. We implemented the multi-hop routing method using the Star-Mesh networks. Also, we use the sensor devices which are satisfied with the IEEE 802.15.4 specification. The used devices are the Nano-24 modules in Octacomm Co. Ltd. A RSSI is very important factor in position recognition analysis. It makes use of the way that decides position recognition and user identification in narrow indoor space. In experiments, we can analyze properties of the RSSI, draw the parameter about position recognition. The experimental result is that RSSI value is attenuated according to increasing distances. It also derives property of the radio frequency (RF) signal. Moreover, we express the monitoring program using the Microsoft C#. Finally, the proposed methods are expected to protect a sudden death and an accident in home.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.585-591
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• 2016

Recently, indoor location detection has become an important area in the IoT (Internet of Things) environment for various indoor location-based services. In this paper, our proposed method shows that a virtual region can be divided electromagnetically according to specific facilities or services in various IoT application areas called zones. The MLP (Multi-Layer Perceptron) method is applied to recognize the service zone at the current position. The MLP utilized an RSSI (Received Signal Strength Indicator) signal of the BLE (Bluetooth Low Energy) Beacon as input data and made decisions to affiliate the zone of the current region as output. In order to verify the proposed method, we constructed an experimental environment similar in size to an actual rail station using four of the beacon and two zones.

• 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.