Disaster-FD: Federated Failure Detection in Disaster Scenarios

This paper explores advanced features of Disaster-FD, a failure detector tailored for disaster-prone environments, with a specific focus on real-time monitoring of Internet of Things (IoT) networks. Leveraging federated monitoring, Disaster-FD enables the monitoring of geographically distributed regions to enhance network resilience. Inspired by Impact-FD, our proposed algorithm incorporates active monitoring and federated capabilities to ensure network reliability under adverse conditions. We conducted comprehensive experiments on the IoT-LAB platform to evaluate the robustness and resilience of Disaster-FD during potential disaster scenarios. These experiments assessed key parameters, including reliability thresholds, confidence levels, and impact factors, while ensuring efficient energy consumption and maintaining high network trust. Extensive evaluations, involving up to four geographically distinct regions in France and nearly a hundred IoT devices, demonstrate the effectiveness of Disaster-FD. Our findings highlight the potential of the algorithm to improve disaster response through enhanced IoT network monitoring, and we outline future directions for further development and optimization.

Authors

Institutions

Publication Details

Journal
Journal of Internet Services and Applications
Published
2025-07-20
DOI
https://doi.org/10.5753/jisa.2025.5876
Primary Topic
Distributed systems and fault tolerance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Disaster-FD: Federated Failure Detection in Disaster Scenarios

Anubis Graciela de Moraes Rossetto, Luciana Arantes, Pierre Sens, Rafael Pasquini et al.
1 links
Journal of Internet Services and Applications
Distributed systems and fault tolerance
article

Disaster-FD: Federated Failure Detection in Disaster Scenarios

Anubis Graciela de Moraes Rossetto, Luciana Arantes, Pierre Sens, Rafael Pasquini, Paulo Coelho, Abadio de Paulo Silva
article en

Abstract

This paper explores advanced features of Disaster-FD, a failure detector tailored for disaster-prone environments, with a specific focus on real-time monitoring of Internet of Things (IoT) networks. Leveraging federated monitoring, Disaster-FD enables the monitoring of geographically distributed regions to enhance network resilience. Inspired by Impact-FD, our proposed algorithm incorporates active monitoring and federated capabilities to ensure network reliability under adverse conditions. We conducted comprehensive experiments on the IoT-LAB platform to evaluate the robustness and resilience of Disaster-FD during potential disaster scenarios. These experiments assessed key parameters, including reliability thresholds, confidence levels, and impact factors, while ensuring efficient energy consumption and maintaining high network trust. Extensive evaluations, involving up to four geographically distinct regions in France and nearly a hundred IoT devices, demonstrate the effectiveness of Disaster-FD. Our findings highlight the potential of the algorithm to improve disaster response through enhanced IoT network monitoring, and we outline future directions for further development and optimization.

Journal of Internet Services and ApplicationsVol. 16(1)
Centre National de la Recherche Scientifique (FR), Sorbonne Université (FR), Instituto Federal Sul-rio-grandense (BR), Universidade Federal de Uberlândia (BR)
Agence Nationale de la Recherche, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Openalex Percentile: Top 76%
Distributed systems and fault tolerance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Disaster-FD: Federated Failure Detection in Disaster Scenarios — Anubis Graciela de Moraes Rossetto, Luciana Arantes, et al. · Journal of Internet Services and Applications (2025) | TGRS Research Map | TGRS