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Design of Distributed Simulation Training System Based on IoT Technology

Published: 20 October 2020 Publication History

Abstract

The distributed missile simulation training system has several parts whose controller is placed far away from each other. The upper control computer needs some communication protocol with the distributed controller and the Internet of Things (IoT) technology can be used in the system. In this research, the communication topology based on RS-485 bus and LoRa WAN is designed. RS-485 bus including interface circuits, Lora interface circuits, transmission protocol, Cyclic Redundancy Check (CRC) method and test software was designed and proposed. And the designed network was tested. The results show that the communication is effective using the RS-485 and LoRa protocol and has a strong stability and a better communication capability of long distance with a complex environment before the missile launching.

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      CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering
      October 2020
      1038 pages
      ISBN:9781450377720
      DOI:10.1145/3424978
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 October 2020

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      Author Tags

      1. IoT
      2. LoRa
      3. RS-485
      4. Transmission protocol

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      CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;
      Overall Acceptance Rate 368 of 770 submissions, 48%

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