The sustainability of providing continuous information after a major disaster has become increasingly challenging due to failures in mainstream communications infrastructure. Natural disasters, such as hurricanes, earthquakes, or floods, can severely damage traditional communication systems, including telephone lines and cellular networks, making disseminating critical information to affected areas difficult.
The high dependency on the internet for sharing information exacerbates the problem. In disaster-stricken regions, internet connectivity can be disrupted or completely unavailable, further impeding communication and information flow. As a result, there is a pressing need to research alternative telecommunication technologies that can function independently of existing infrastructure. These technologies should be resilient, reliable, and capable of providing communication services in disaster scenarios.
Overall, researching and implementing alternative telecommunication technologies that can function independently of mainstream infrastructure is crucial for ensuring the sustainability of providing continuous information after large-scale disasters. These technologies offer resilience, reliability, and the ability to keep affected communities connected when needed. Therefore, CRISiSLab aims to explore potential alternative communication technologies and protocols resilient of maintaining continuous information follow after disasters or emergencies.
Feasibility of LPWAN based Communication Architecture for Earthquake Early Warning (EEW) and Building/infrastructure Monitoring Systems
Description: This research project aligns and is complementary to our ongoing research on earthquake early warning (EEW) and building and infrastructure monitoring systems where we propose exploring the feasibility of using LPWAN ( Low Power Wide Area Networks) based communication technologies such as LoRa (Long Range) and SigFox as a reliable and sustainable solution to overcome the limited or no access to the Internet during a large earthquake scenario in the context of Aotearoa New Zealand. Investigators of this proposal have been successfully exploring the use of micro-electromechanical (MEMS) based sensor networks for EEW and building and infrastructure monitoring solutions. Currently, these systems rely heavily on the Internet for sensor communication. Observing these systems’ performance and the related literature on such systems in other parts of the world clearly indicates the vulnerability when operating with limited or no Internet connectivity. Such limitations justify the need of conducting the research proposed. The successful achievement of the aim and objectives of the proposed research will contribute to expanding the ongoing research, product development and capability development of CRISiSLab and its collaborators and students. Further, it will contribute to more reliable and sustainable EEW and building and infrastructure monitoring solutions.
Led by: Raj Prasanna
Hong, B., Chandrakumar, C., Ravishan, D., Prasanna, R., (2022). A Peer-to-Peer Communication Method for Distributed Earthquake Early Warning Networks: Preliminary Findings. Proceedings of the ISCRAM Asia Pacific Conference 2022. https://idl.iscram.org/files/benjaminhong/2023/2485_BenjaminHong_etal2023.pdf
Prasanna, R., Chandrakumar, C., Nandana, R., Holden, C., Punchihewa, A., Becker, J.S., Jeong, S., Liyanage, N., Ravishan, D., Sampath, R., Tan, M.L. (2022). “Saving precious seconds”—A novel approach to implementing a low-cost earthquake early warning system with node-level detection and alert generation. Informatics. https://doi.org/10.3390/informatics9010025
Imtiaz, SY., Uma, SR., Prasanna, R., Wotherspoon, LM. (2021). ‘End to end’ linkage structure for integrated impact assessment of infrastructure networks under natural hazards. Bulletin of the New Zealand Society for Earthquake Engineering. https://doi.org/10.5459/bnzsee.54.2.153-162
Uma, S.R., Syed, Y., Sapthala, K., Prasanna. R. (2020). Modelling interdependencies of critical infrastructure network recovery using a decision support system. GNS Science Report. https://shop.gns.cri.nz/sr_2020-18-pdf/