Disaster resilient mesh network using LoRa and Nervenet

When a natural disaster event happens, it could cause regional cellular network outages and hence disable network communication within the affected area. If a resilient network is implemented, alert messages with sufficient information can be sent over the Internet to provide a nationwide response....

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Main Author: Lean, Chee Hong
Format: Final Year Project / Dissertation / Thesis
Published: 2022
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Online Access:http://eprints.utar.edu.my/4968/1/ET_1702976_FYP_report_%2D_CHEE_HONG_LEAN.pdf
http://eprints.utar.edu.my/4968/
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spelling my-utar-eprints.49682022-12-23T13:34:08Z Disaster resilient mesh network using LoRa and Nervenet Lean, Chee Hong TK Electrical engineering. Electronics Nuclear engineering When a natural disaster event happens, it could cause regional cellular network outages and hence disable network communication within the affected area. If a resilient network is implemented, alert messages with sufficient information can be sent over the Internet to provide a nationwide response. Japan National Institute of Information and Communication Technology has invented a resilient network framework called NerveNet, it supports mesh network where each node will approach other nodes in range if the current peer no longer responds. Using their technology, disaster nodes could be installed at disaster hotspots to send out disaster information or even provide light internet services. NerveNet does support data communication using Wi-Fi and LoRa. NerveNet Wi-Fi-Mesh links are used to provide wide bandwidth but low range data transmission, while NerveNet LoRa-Mesh supports narrow bandwidth data transmission in coverage of kilometers, which is suitable for crucial or emergency disaster data updates. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4968/1/ET_1702976_FYP_report_%2D_CHEE_HONG_LEAN.pdf Lean, Chee Hong (2022) Disaster resilient mesh network using LoRa and Nervenet. Final Year Project, UTAR. http://eprints.utar.edu.my/4968/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Lean, Chee Hong
Disaster resilient mesh network using LoRa and Nervenet
description When a natural disaster event happens, it could cause regional cellular network outages and hence disable network communication within the affected area. If a resilient network is implemented, alert messages with sufficient information can be sent over the Internet to provide a nationwide response. Japan National Institute of Information and Communication Technology has invented a resilient network framework called NerveNet, it supports mesh network where each node will approach other nodes in range if the current peer no longer responds. Using their technology, disaster nodes could be installed at disaster hotspots to send out disaster information or even provide light internet services. NerveNet does support data communication using Wi-Fi and LoRa. NerveNet Wi-Fi-Mesh links are used to provide wide bandwidth but low range data transmission, while NerveNet LoRa-Mesh supports narrow bandwidth data transmission in coverage of kilometers, which is suitable for crucial or emergency disaster data updates.
format Final Year Project / Dissertation / Thesis
author Lean, Chee Hong
author_facet Lean, Chee Hong
author_sort Lean, Chee Hong
title Disaster resilient mesh network using LoRa and Nervenet
title_short Disaster resilient mesh network using LoRa and Nervenet
title_full Disaster resilient mesh network using LoRa and Nervenet
title_fullStr Disaster resilient mesh network using LoRa and Nervenet
title_full_unstemmed Disaster resilient mesh network using LoRa and Nervenet
title_sort disaster resilient mesh network using lora and nervenet
publishDate 2022
url http://eprints.utar.edu.my/4968/1/ET_1702976_FYP_report_%2D_CHEE_HONG_LEAN.pdf
http://eprints.utar.edu.my/4968/
_version_ 1753792999096057856
score 13.160551