• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.339-346
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• 2011

In the existing RF communication based WPAN environment, a lowering of battery span and interference problem among sensors occur because the value of output is set and transmitted steadily when the system on sensor is initialized. Therefore, this paper proposes a framework and a transmit method with low power which decreases the electricity consumption by properly controling transmit power of opponent by received signal strength indicator(RSSI) of each sensor. The system proposes a power-lowering method by controling transmit power properly by the transmit intensity of the connected sensor after being affected by the transmit intensity of surrounded sensor. The framework that is proposed in this paper includes data transmit module, transmit power manager module, transmit power searching module, signal transmit module, and signal receiving module.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4766-4786
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• 2016

Looking for missing companions who are out of touch in public places might suffer a long and painful process. With the help of mobile crowdsourcing, the missing person's location may be reported in a short time. In this paper, we propose MissingFound, an assistant system that applies mobile crowdsourcing for finding missing companions. Discovering valuable users who have chances to see the missing person is the most important task of MissingFound but also a big challenge with the requirements of saving battery and protecting users' location privacy. A customized metric is designed to measure the probability of seeing, according to users' movement traces represented by WiFi RSSI fingerprints. Since WiFi RSSI fingerprints provide no knowledge of users' physical locations, the computation of probability is too complex for practical use. By parallelizing the original sequential algorithms under MapReduce framework, the selecting process can be accomplished within a few minutes for 10 thousand users with records of several days. Experimental evaluation with 23 volunteers shows that MissingFound can select out the potential witnesses in reality and achieves a high accuracy (76.75% on average). We believe that MissingFound can help not only find missing companions, but other public services (e.g., controlling communicable diseases).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4133-4137
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• 2011

Reliable Multi-Reader RFID identification is one of issues in Multi-Reader RFID realization program in recent. And the Multi-Reader RFID reader has difficulty to obtain reliable data in data capture layer. The reason is that unreliable readings such as a false positive reading and a false negative reading and missed readings can happen by reader collision problems, noise, or the mobility of tagged objects. We introduce performance metrics to solve these reader problems. We propose three solutions the Minimum Overlapped Read Zone (MORZ) with Received Signal Strength Indicator (RSSI), the Spatial-Temporal Division Access (STDA) method, and double bigger size of tags attached on the object. To show the improvement of the proposed methods, we calculate tag's successful read rates in a smart office, which consists of Multi-Reader RFID systems.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.207-212
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• 2011

Vehicular Ad Hoc Network(VANET) is a communication technology between vehicles and vehicles(V2V) or vehicles and infrastructures(V2I) for offering a number of practical applications. Considering the importance of communicated information through VANET, data authentication, confidentiality and integrity are fundamental security elements. Recently, to enhance a security of VANET in various circumstances, message authentication is widely researched by many laboratories. Among of them, Zhang. et. al. is an efficient method to authenticate the message with condition of anonymity in dense space. In the scheme, to obtain the vehicular ID with condition of anonymity, the k-anonymity is used. However it has a disadvantage, which conducts hash operations in case of determining the vehicular ID. In the paper, we present a location based algorithm using received signal strength for the location based authentication and encryption technique as well, and to enhance the accuracy of algorithm we apply a location determination technique over the 3-dimensional space.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.109-116
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• 2014

In recent, ISM (Industrial Scientific Medical) band that is 2.4GHz band authorized free of charge is being widely used for smart phone, notebook computer, printer and portable multimedia devices. Accordingly, studies have been continuously conducted on the possibility of coexistence among nodes using ISM band. In particular, the interference of IEEE 802.11b based Wi-Fi device using overlapping channel during communication among IEEE 802.15.4 based wireless sensor nodes suitable for low-power, low-speed communication using ISM band causes serious network performance deterioration of wireless sensor networks. This paper examined a method of identifying channel status to avoid interference among wireless communication devices using IEEE 802.11b (Wi-Fi) and other ISM bands during communication among IEEE 802.15.4 based wireless sensor network nodes in ISM band. To identify channels occupied by Wi-Fi traffic, various studies are being conducted that use the RSSI (Received Signal Strength Indicator) value of interference signal obtained through ED (Energy Detection) feature that is one of IEEE 802.15.4 transmitter characteristics. This paper examines an algorithm that identifies the possibility of using more accurate channel by mixing utilization of interference signal and RSSI mean value of interference signal by wireless sensor network nodes. In addition, it verifies such algorithm by using OPNET Network verification simulator.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.786-791
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• 2021

In this paper, in order to minimize limitations such as signal interference and positioning errors in existing indoor positioning systems, a smart LED-based positioning system for excellent line-of-sight radio environments and precise location tracking is proposed to improve accuracy. An IEEE 802.4 Zigbee module is mounted on the SMPS board of a smart LED; RSSI and LQI signals are received from a moving tag, and the system is configured to transmit the measured data to the positioning server through a gateway. For the experiment, the necessary hardware, such as the gateway and the smart LED module, were separately designed, and the experiment was conducted after configuring the system in an external field office. The positioning error was within 70cm as a result of performing complex calculations in the positioning server after transmitting a vector value of the moving object obtained from the direction sensor, together with a signal from the moving object received by the smart LED. The result is a significantly improved positioning error, compared to an existing short-range wireless communications-based system, and shows the level at which commercial products can be implemented.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1551-1558
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• 2021

Beacon signals used in indoor positioning system are reflected and distorted, resulting in noise signals. KF(Kalman Filter) has been widely used to remove this noise. In order to apply the KF, optimization process considering the signal type, signal strength, and environmental elements of each product is required. In this paper, we propose a solution to the optimization problem of KF Parameters using GA(Genetic Algorithm) in BLE(Bluetooth Low Energy) Beacon-based indoor positioning system. After optimizing KF Parameters by applying the proposed technique with a certain distance between Beacon and receiver, we compared the estimated distance passed through KF with the unfiltered distance. The proposed technique is expected to reduce the time required and improve accuracy of KF Parameters optimization in an indoor positioning system based on RSSI (Received Signal Strength Indication).

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1855-1862
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• 2022

Various indoor location-based services can be provided through indoor presence determination and indoor positioning technology using Beacon. However, since the BLE signal advertised by the beacon has an unstable RSSI due to problems such as multi-path fading, it is difficult to guarantee the accuracy of indoor presence determination. In this paper, data were collected while the classroom door was open to ensure accuracy in various situations. Based on the collected data, we propose an indoor presence determination method considering the characteristics of the signal. The proposed method uses support vector machine, showed about 10% accuracy improvement compared to the results using raw RSSI only. This method has the advantage of being able to accurately determine indoor presence with only one receiver. It is expected that the proposed method can implement a low-cost system for determining indoor presence with high accuracy.