• Journal of IKEEE
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• v.25 no.2
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• pp.252-260
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• 2021

In this paper, a method of using Software Defined Radio (SDR) is proposed for improving the accuracy of identifying two kinds of signals as Wireless Fidelity (Wi-Fi) signal and Bluetooth signal at the same frequency band of 2.4 GHz based on the time-domain signal characteristic. An SDR device was set up for collecting transmitting signals from Wi-Fi access points (Wi-Fi) and mobile phones (Bluetooth). Different characteristics between Wi-Fi and Bluetooth signals were extracted from the measured result. The SDR device is programmed with a Wi-Fi and Bluetooth detection algorithm and a collision detection algorithm to detect and verify the Wi-Fi and Bluetooth signals based on collected IQ data. These methods are necessary for some applications like wireless communication optimization, Wi-Fi fingerprint localization, which helps to avoid interference and collision between two kinds of signals.

• Journal of Digital Contents Society
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• v.14 no.3
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• pp.349-356
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• 2013

Products of Wi-Fi devices in recent years offer higher throughput and have longer signal coverage which also bring unnecessary signal interference to neighboring wireless networks, and result in decrease of network throughput. Signal interference is an inevitable problem because of the broadcast nature of wireless transmissions. However it could be optimized by reducing signal coverage of wireless devices. On the other hand, smaller signal coverage also means lower transmission power and lower data throughput. Therefore, in this paper, we analyze the relationship among signal strength, coverage and interference of Wi-Fi networks, and as a tradeoff between transmission power and data throughput, we propose a range-aware Wi-Fi network scheme which controls transmission power according to positions and RSSI(Received Signal Strength Indication) of Wi-Fi devices and analyze the efficiency of the proposed scheme by simulation.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.395-402
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• 2023

The implementation of a hybrid network utilizing Power Line Communication (PLC) and Wi-Fi technologies has been demonstrated to improve signal strength and coverage in areas with poor connectivity due to internet shadow areas. In this study we strategically positioned Wi-Fi relays and utilized the capabilities of PLC technology to significantly improve signal strength and coverage in areas with poor connectivity. We also analyzed the effects of metallic obstacles on Wi-Fi signal propagation and proposed a solution to strengthen the signal enough to pass through them. Our experiment demonstrated the feasibility and potential of using this hybrid network in industrial scenarios for real-time data transmission. Overall, the results suggest that the use of PLC and Wi-Fi hybrid networks can be a cost-effective and efficient solution for overcoming internet connectivity challenges and has the potential to provide high-speed internet access to areas with unreliable signals.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.63-68
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• 2022

In schools and corporate buildings, public WiFi Access Points are installed on the ceilings of hallways. In the case of an architectural structure in which a WiFi signal enters through a steel door made of a material with high signal attenuation, Internet connection is frequently cut off or fails when the door is closed. To solve this problem, our research implements an economical and portable WiFi extender using a Raspberry Pi and an auxiliary battery. Commercially available WiFi extenders have limitations in the location where the power plug is located, and WiFi extension using the WiFi hotspot function of an Android smartphone is possible only in some high-end models. However, because the proposed device can be installed at the position where the Wi-Fi reception signal is the best inside the door, the WiFi range can be extended while minimizing the possibility of damage to the original signal. Experimental results show that it is possible to eliminate the shadows of radio waves and to provide Internet services in the office when the door is closed, to the extent that web browsing and real-time video streaming for 720p are possible.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.38-44
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• 2021

Wi-Fi provides some low-power connection solutions that other Bluetooth cannot provide, and at the same time brings many benefits. First, there is a potentially higher data rate: it can reach 230mbps. Wi-Fi coverage is also wider than competitors, and its operating frequency is also 5GHz, which is much less congested than 2.4GHz. Finally, it also supports IP networks, which is important if you want to send data to the cloud without complexity. The 802.11ac standard of the previous generation still accounts for most shipments (80.9%) and revenue (76.2%). However, there is a limit to accepting IoT devices that will continue to increase significantly in the future. To solve this problem, the new Wi-Fi 6 standard is expected to be the solution (IEEE 802.11ax) which is quickly becoming the main driving force of the wireless local area network (WLAN) market. According to IDC market research analysts, in the first quarter of 2020, independent access points (APs) supported by Wi-Fi 6 accounted for 11.8% of shipments, but 21.8% of revenue. In this paper, we have compared and analyzed the IoT connectivity, QoS, and security requirements of devices using Wi-Fi 6 network.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.154-161
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• 2018

As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.16-19
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• 2014

In this paper, the beacon signals of WiFi doppler frequency detection based WiFi-AP was designed with the subcarrier between a transmitter and a receiver of WLAN(wireless LAN). We can use such signals to identify human moving as an antenna array and tracking of RF beam. This wireless security services with the combination of WiFi doppler frequency and adaptive beacon time signal was proposed for wireless detection and motion based services.