Designing a fault-tolerant swarm of drones for flash flood risk assessment

About this position

Research fields: Distributed Systems, Autonomous Systems, Environmental Monitoring Impacts

Keywords: Fault Tolerance, Distributed Algorithms, distributed Systems, Wireless Networks, Embedded Systems, Cyber-Physical Systems, Drones, UAV Swarm, Environmental Monitoring and Modelling, Flash Flood

Motivation

As flash floods have become a recurring disaster[1], causing loss of life, damage to agriculture and infrastructure, and disruption to communities all over the world, more detailed modelling and monitoring of rainfall and riverine floods is key to accurately assessing the risk of increasingly frequent water-related natural hazards. Our doctoral project brings together hydrologists and computer science experts to explore modelling techniques[2,3] and novel UAV-based remote sensing methods[4,5]. The aim of this project is to improve our understanding of the phenomenon and its impacts, and to enhance the spatial

and temporal resolution, particularly for localised events in densely populated areas.

Context, state-of-the-art research and scientific goals

In the face of rapid climate change, the water cycle is set to be one of the most affected of all the Earth’s ecological systems. Therefore, more detailed modelling and monitoring of rivers is key to accurately assessing the risk of increasingly frequent water-related natural hazards, such as flash floods[6] induced by rainfall. In this context, several remote sensing techniques and modelling frameworks have been employed to facilitate environmental monitoring and study the ongoing impact of human-induced climate change on hydrological cycles.

A large body of research uses satellite remote sensing to provide a global perspective on the Earth. However, the intrinsic spatio-temporal resolution limitations of satellite platforms impose fundamental constraints on improving the precision of water monitoring

systems.

To address these limitations, the use of cheaper, more flexible monitoring platforms based on drone swarms at very low altitudes has emerged as a promising complementary approach for collecting higher-resolution data through more flexible sampling methodologies. Indeed, airborne remote sensing using drone swarms can enable richer on-demand data collection and faster processing at the edge. This precise, high-resolution, real-time information on rapidly evolving and potentially dangerous rivers will help increase our understanding of these phenomena, leading to better forecast and, ultimately, better-informed decision-making during crisis response.

While this technique shows great promise, experiments on concrete use cases are needed to better understand the advantages and limitations of drone swarms for hydrology and flood forecast.

This joint doctoral research project aims to provide an innovative, drone-based solution to the increasingly frequent problem of flash flooding and the lack of precise spatial and temporal observation resolution for surveying it. The project will provide an opportunity to design novel modelling techniques and advanced sensor technologies for emerging, very high-resolution remote sensing applications on autonomous drone swarms. This includes open research problems in mobile edge computing, environmental monitoring, urban hydrological and climate modelling, and data collection for natural hazards such as flood risk prediction and assessment.

Other areas of research include wireless communication, mission planning and unmanned aircraft system traffic management and regulations for autonomous drone swarms. To this end, this interdisciplinary project combines the collection and modelling of river hydrological data with the design of fault-tolerant distributed systems for remote sensing using drone swarms.

References

[1] Tabari, Hossein. ”Climate change impact on flood and extreme precipitation increases with water availability. Scientific reports 10.1 (2020): 13768.

[2] Lima Neto, Otacı́lio Correia, et al. ”Sub-daily hydrological-hydrodynamic simulation in flash flood basins: Una river (Pernambuco/Brazil). Revista Ambiente Água 15 (2020): e2556.

[3] Roozbahani, Abbas, Parichehreh Behzadi, and Alireza Massah Bavani. ”Analysis of performance criteria and sustainability index in urban stormwater systems under the impacts of climate change. Journal of Cleaner Production 271 (2020): 122727.

[4] Koutalakis, Paschalis, Ourania Tzoraki, and George Zaimes. ”UAVs for hydrologic scopes: Application of a low-cost UAV to estimate surface water velocity by using three different image-based methods. Drones 3.1 (2019): 14.

[5] Bagnato, Alessandra, et al. ”Designing swarms of cyber-physical systems: the H2020 CPSwarm project. Proceedings of the Computing Frontiers Conference. 2017.

[6] Dottori, Francesco, et al. ’Cost-effective adaptation strategies to rising river flood risk in Europe.’

Nature Climate Change 13.2 (2023): 196-202.