UTAR Institutional Repository

Disaster Resilient Mesh Network With Data Synchronization Using Nervenet

Lim, Wei Sean (2021) Disaster Resilient Mesh Network With Data Synchronization Using Nervenet. Final Year Project, UTAR.

Download (2196Kb) | Preview


    Natural disasters occur frequently around the world. Internet of Things (IoT) sensors such as video cameras can detect such cataclysmic events, track the number of victims and subsequently initiate rescue actions. How to disseminate the critical information, however, remains an open issue especially when there are communication breakdowns. This project aims to develop a regional disaster response platform using NerveNet, which is a mesh networking technology provided by Japan NICT. By utilising NerveNet Hearsay daemon, images can be wirelessly synchronized in multiple NerveNet nodes’ database. To facilitate the emergency management, a cloud monitoring dashboard to visualize multiple regional response and monitoring networks has been designed and developed. Serving as a proof of concept, a NerveNet testbed consisting of two base stations and one gateway has been implemented. Experimental results validate the feasibility of the proposed platform from two perspectives, namely network and data synchronization performance. The former measures throughput, delay, and jitter, whereas the latter focuses on analysing the latency of image synchronization. The project findings can serve as the guideline for designing a disaster response and monitoring platform in not only Malaysia but also other ASEAN countries.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Lee Kong Chian Faculty of Engineering and Science > Bachelor of Engineering (Honours) Electrical and Electronic Engineering
    Depositing User: Sg Long Library
    Date Deposited: 12 Jun 2021 05:26
    Last Modified: 12 Jun 2021 05:26
    URI: http://eprints.utar.edu.my/id/eprint/4057

    Actions (login required)

    View Item