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Analysis of Limits in Applying AP-QoS-based Wi-Fi Slicing for Real-Time Systems
Jin Hyun Kim, Hyonyoung Choi, Gangjin Kim, Yundo Choi, Tae-Won Ban, Se-Hoon Kim
http://doi.org/10.5626/JOK.2021.48.6.723
Network slicing is a new network technology that guarantees the quality of network services according to application services or user’s types. Wi-Fi, IEEE 802.11-based LAN, is the mostly popularly used short-range wireless network and has been continually attracting more and more from users. Recently, the use of Wi-Fi by safety critical IoT devices, such as medical devices, has been drastically increasing. Moreover, enterprises require network slicing of Wi-Fi to introduce the provision of prioritized QoS of Wi-Fi depending on the service type of customer. This paper presents the analysis of the limits and difficulties in applying AP-QoS-based network slicing for hard real-time systems that demand temporal deterministic streaming services. In this paper, we have defined a formal framework to analyze QoS-providing IEEE 802.11e Enhanced Distributed Coordination Access and provide the worst-case streaming scenarios and thereby demonstrated why the temporal determinism of network streaming is broken. In addition, simulation results of AP-QoS-based network slicing using NS-3 are presented to show the limits and difficulties of the network slicing. Moreover, we present Wi-Fi network slicing techniques based on EDCA of AP-QoS for real-time systems through our technical report referenced in this paper.
Study and Application of RSSI-based Wi-Fi Channel Detection Using CNN and Frequency Band Characteristics
Junhyun Park, Hyungho Byun, Chong-Kwon Kim
http://doi.org/10.5626/JOK.2020.47.3.335
For mobile devices, Wi-Fi channel scanning is essential to initiating an internet connection, which enables access to a variety of services, and maintaining a stable link quality by periodic monitoring. However, inefficient Wi-Fi operation, where all channels are scanned regardless of whether or not an access point (AP) exists, wastes resources and leads to performance degradation. In this paper, we present a fast and accurate Wi-Fi channel detection method that learns the dynamic frequency band characteristics of signal strengths collected via a low power antenna using a convolution neural network (CNN). Experiments were conducted to demonstrate the channel detection accuracy for different AP combination scenarios. Furthermore, we analyzed the expected performance gain if the suggested method were to assist the scanning operation of the legacy Wi-Fi.
Graph-based Wi-Fi Radio Map Construction and Update Method
http://doi.org/10.5626/JOK.2017.44.6.643
Among Wi-Fi based indoor positioning systems, fingerprinting localization is the most common technique with high precision. However, construction of the initial radio map and the update process require considerable labor and time effort. To address this problem, we propose an efficient method that constructs the initial radio map at each vertex based on a graph. In addition, we introduce a method to update the radio map automatically by mapping signal data acquired from users to the reference point created on each edge. Since the proposed method collects signal data manually only at the vertex of the graph to build the initial radio map and updates it automatically, our proposed method can dramatically reduce labor and time effort, which are the disadvantages of the conventional fingerprinting method. In our experimental study, we show validity of our radio map update method by comparing with the actual reference point data. We also show that our proposed method is able to construct the radio map with an accuracy of about 3.5m by automatically updating the radio map.
Efficient Packet Transmission Utilizing Vertical Handover in IoT Environment
The Internet of Things (IoT) has recently been showered with much attention worldwide. Various kinds of devices, communicating with each other in the IoT, demand multiple communication technologies to coexist. In this environment, mobile devices may utilize the vertical handover between different wireless radio interfaces such as Wi-Fi and Bluetooth, for efficient data transfer. In this paper, an IoT broker is implemented to support the vertical handover, which can also support and manage heterogeneous devices and communication interfaces. The handover is activated based on RSSI, Link Quality values, and real time traffic. The experimental results show that the proposed handover system substantially improves QoS in Bluetooth and reduces power consumption in mobile devices as compared with a system using only Wi-Fi.
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