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Project Introduction

Natural disasters occur frequently around the world. Internet of Things (IoT) sensors can detect such cataclysmic events, but these data are often analysed in the cloud. This project aims to develop a context aware disaster mitigation system (CAMS) that utilizes mobile edge computing (MEC) and a wireless mesh network powered by NerveNet. Armed with MEC, each IoT node executes AI detection tasks locally and submits metadata comprising the disaster content to a gateway. The gateway eliminates disaster content redundancy by performing cluster traffic scheduling based on disaster activity level. Such critical information is stored in the NerveNet distributed database. Using wireless mesh networking, data can be disseminated to emergency response units (ERU) for rescue planning.
 

Project Members

* Project Leader
Project Members
Full Name Position/Degree Department, Institution, Country
Tham Mau Luen* Assistant Professor/PhD Department of Electrical and Electronic Engineering, UTAR, Malaysia
Chang Yoong Choon Associate Professor/Head of Department/PhD Department of Electrical and Electronic Engineering, UTAR, Malaysia
Lee Ying Loong Assistant Professor/PhD Department of Electrical and Electronic Engineering, UTAR, Malaysia
Ezra Morris Associate Professor/Deputy Director/PhD Institute of Postgraduate Studies and Research, UTAR, Malaysia
Teoh Han Wei Master Student Institute of Postgraduate Studies and Research, UTAR, Malaysia
Lim Wei Sean Undergraduate Institute of Postgraduate Studies and Research, UTAR, Malaysia
Nathaniel Tan Sze Yang Master Student Institute of Postgraduate Studies and Research, UTAR, Malaysia
Wong Yi Jie PhD Student Institute of Postgraduate Studies and Research, UTAR, Malaysia
Homayun Kabir PhD Student Institute of Postgraduate Studies and Research, UTAR, Malaysia
Jiang Sheng Qi PhD Student Institute of Postgraduate Studies and Research, UTAR, Malaysia
Nordin Bin Ramli Senior Staff Researcher/PhD MIMOS, Malaysia
Tuan Ahmad Zahidi Tuan Abdul Rahman Senior Engineer MIMOS, Malaysia
Yasunori Owada Senior Researcher/PhD National Institute of Information and Communications Technology (NICT), Japan
Goshi Sato Researcher/PhD National Institute of Information and Communications Technology (NICT), Japan
Myint Myint Sein Professor/Head of Department/PhD Geographical Information System, University of Computer Studies, Yangon, Myanmar
Thin Lai Lai Thein Professor/ PhD Geographical Information System, University of Computer Studies, Yangon, Myanmar
Zin May Aye Professor University of Computer Studies, Yangon, Myanmar
Emmon Maw PhD student University of Computer Studies, Yangon, Myanmar
Ye Naing PhD student University of Computer Studies, Yangon, Myanmar
Nay Win Aung PhD student University of Computer Studies, Yangon, Myanmar
Theint Theint PhD student University of Computer Studies, Yangon, Myanmar
Suvit Poomrittigul Associate Dean/PhD Faculty of Science and Technology, Pathumwan Institute of Technology, Thailand
Sakda Sakorntanant Lecturer Faculty of Science and Technology, Pathumwan Institute of Technology, Thailand

Associate Project Members
Full Name Position/Degree Department, Institution, Country
Hachihei Kurematsu Senior Vice President/Bachelor BHN Association (NGO)/ Japanese Telemedicine and Telecare Association (JTTA), Japan
Nobuyuki Asai CEO Ready Affiliate Japan co., Ltd.

Project Introduction[PDF 204KB]

November 2020 Progress Review[PDF 476KB]

GCCE conference 2021

GCCE Conference 2021 Presentation and Report[PDF 2.14MB]

IEEE UEMCON 2021 Conference

IEEE UEMCON 2021 Conference Presentation Report[PDF 257KB]

IEEE UEMCON 2021 Conference Paper - Joint Disaster Classification and Victim Detection using Multi-Task Learning[PDF 598KB]

The 13th International Conference on Ubiquitous and Future Networks

Efficient Device-Edge Inference for Disaster Classification Presentation Report[PDF 275KB]

Efficient Device-Edge Inference for Disaster Classification Presentation (PPT)

ICITM 2023

ICITM 2023 Conference Report[Word 633KB]

ICITM 2023 Conference Programme[PDF 2.81MB]

ICITM 2023 Presentation Slides[PDF 2.47MB]

Flood Forecasting using Edge AI and LoRa Mesh Network - Paper[PDF 424KB]

Publication abstract and references[PDF 785KB]

ICBDSC 2023 Conference

Conference report[Word 270KB]

ICBDSC 2023 Conference programme[PDF 1.72MB]

Best presentation certificate[PDF 793KB]

ICBDSC 2023 Presentation slides[PDF 2.32MB]

Sensors (journal)

Journal report[Word 130KB]

Journal cover page[PDF 3.75MB]

Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment[PDF 7.21MB]

Journal article banner[PDF 26.3KB]

IEEE Access

An Optimized Multi-Task Learning Model for Disaster Classification and Victim Detection in Federated Learning Environments[PDF 5.35MB]

IEEE Access general report[Word 132KB]

6th Conference on Cloud and Internet of Things

Paper Presentation Report[Word 248KB]

CIoT 2023 full programme[PDF 155KB]

CIoT 2023 presentation slides[PDF 3.04MB]