[논문]A Study on Intelligent Edge Computing Network Technology for Road Danger Context Aware and Notification

Oh, Am-Suk

doi:10.6109/jicce.2020.18.3.183

A Study on Intelligent Edge Computing Network Technology for Road Danger Context Aware and Notification 원문보기

Journal of information and communication convergence engineering, v.18 no.3, 2020년, pp.183 - 187

Oh, Am-Suk (Department of Digital Media Engineering, Tongmyong University)

Abstract ▼ AI-Helper

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

주제어

표/그림 (9)

그림 Fig. 1. Existing Wi-Fi network architecture.
그림 Fig. 2. Intelligent edge computing network architecture.
그림 Fig. 3. Intelligent autonomous cluster network structure.
그림 Fig. 4. Area classification according to signal century.
그림 Fig. 5. AP Area/STA Area.
그림 Fig. 6. Friis equation for deriving Link Budget [13].
그림 Fig. 7. Existing network disconnection due to failure.
그림 Fig. 8. When added outside the effective range of the AP.
그림 Fig. 9. Intelligent edge computing IoT device connection diagram.

AI 본문요약
AI-Helper

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문제 정의

In this paper, we researched the network technology of the intelligent edge that autonomously builds a network using intelligent IoT devices in the network edge environment and the development of a real-time road hazard recognition and notification system using intelligent IoT device technology. As a result of this paper, intelligent edge computing network technology is highly important in that it will ultimately contribute greatly to improving national competitiveness through the development effect of related academic fields, securing social safety nets, and economic ripple effects.
In this paper, we study intelligent edge computing network technology that autonomously builds a network using intelligent IoT devices in a network edge environment, and a realtime road hazard recognition and notification system applying intelligent IoT device technology [9, 10].

제안 방법

Research is conducted on how to utilize RNN (Recurrent Neural Network), which has excellent performance in processing time series data with algorithm that is intended to be applied. In particular, the algorithms intended to be applied to this study are intended to study the utilization of LSTM(long short-term memory), one of the recursive neural networks.
In this detailed study, we study an algorithm that selfrecovers faults when a fault condition occurs during operation of the IoT devices that make up the network. Network failures Situation is classified into cases in which empty space is created in the network due to the sudden stop of AP mode Fig.
In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road hazard recognition and notification system through an intelligent risk situation recognition algorithm. Fig.
In this paper, we divide into AP Area/ STA Area as shown in Fig. 5 on the basis of signal strength of the AP, and study AP/STA mode switching techniques that support flexible connections between devices by switching into the appropriate mode when new devices enter each area. STA Area is an area where STA can be stably connected when a new IoT device is added, and AP Area refers to an area outside the AP's STA Area where signal strength is weakened and network scope expansion is needed.
Based on the secured traffic situation information, we develop an algorithm that can intelligently recognize the traffic flow and accident situation through an intelligent traffic situation recognition algorithm. Research is conducted on how to utilize RNN (Recurrent Neural Network), which has excellent performance in processing time series data with algorithm that is intended to be applied. In particular, the algorithms intended to be applied to this study are intended to study the utilization of LSTM(long short-term memory), one of the recursive neural networks.

이론/모형

6. In this study, the value is derived through the Friis power transmission equation for transmission and reception in the link design of the radio communication system. Link Budget is the performance required (gain, loss, BER, etc.
The learning algorithm of the LSTM recursive neural network used in this study is developed using the BPTT (Back Propagation Through Time) algorithm. The gradient of the error function E for the gate and state storage is learned by calculating the error function E and the partial derivative of the Kth element of each gate and internal state using the following equation to obtain the error gradient.

후속연구

In order to realize C-ITS, advanced road infrastructure for collecting road information along with communication technology between traffic elements is essential, and solutions that can provide accident prevention and prompt response using advanced road infrastructure are needed. Therefore, in this paper, we intend to develop a real-time road hazard situation recognition and notification system for the application of C-ITS based on an intelligent edge computing network [7, 8].

참고문헌 (13)

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K. Mase, H. Okada, and Y. Nakano, "RSSI-based cross layer link quality management for layer 3 wireless mesh networks," in SoftCOM 2009-17th International Conference on Software, Telecommunications & Computer Networks, pp. 101-105, 2009.
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H. M. Park and T. H. Hwang, "Changes and Trends in Edge Computing Technology," Journal of The Korean Institute of Communication Sciences, vol. 36, no. 2, pp. 41-47, 2019.
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상세보기
C. Perera, A. Zaslavsky, P. Christen, and D.Georgakopoulos, "Context aware computing for the internet of things: A survey," IEEE Communications Surveys & Tutorials, vol. 16, no.1, pp. 414-454, 2014.

상세보기
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): A vision, architectural elements, and future directions," Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, 2013.

상세보기
H. H. Yang, "Lode location management using RSSI regression analysis in wireless sensor network," Journal of the Korea Institute of Information and Communication Engineering, vol. 13, no. 4, pp. 1935-1940, 2009.
Chipsen. 2.4Ghz wireless communication (reach) distance estimation [Internet]. Available: https://help.chipsen.com/support/solutions/articles/22000209779-2-4ghz/.

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