Neuromorphic solar edge AI for sustainable wildfire detection

This paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and Br...

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Detalhes bibliográficos
Autor: Parada, R
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Recursos:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p8878
Acesso em linha:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8878
Access Level:acceso embargado
Palavra-chave:Edge AI
Neuromorphic computing
Solar-Powered drones
Wildfire detection
Internet of robotic things
Sustainable monitoring
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spelling Neuromorphic solar edge AI for sustainable wildfire detectionParada, REdge AINeuromorphic computingSolar-Powered dronesWildfire detectionInternet of robotic thingsSustainable monitoringThis paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and BrainChip Akida, integrated into solar-optimized eBee X drones. Results show that the BrainChip Akida achieves 4200 patrol hrs per yr, nearly three times that of traditional CPU systems, while maintaining 87 % solar energy autonomy. The Google Coral TPU and Raspberry Pi 4 reach 66 % and 52 % autonomy, respectively. Fleet scaling analysis demonstrates that increasing drone count from one to eight reduces median wildfire detection time from 18 to 2.2 hrs, surpassing critical response thresholds. Seasonal analysis reveals Akida-based systems can operate fully on solar energy during summer and most of spring and fall, minimizing grid dependency. These findings establish neuromorphic computing as a foundational technology for sustainable, perpetual environmental monitoring within the Internet of Robotic Things (IoRT).ELSEVIER2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8878Internet of ThingsISSN: 25431536ISSNe: 25426605reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/embargoedAccess2027-03-01oai:cttc.fundanetsuite.com:p88782026-06-17T11:44:47Z
dc.title.none.fl_str_mv Neuromorphic solar edge AI for sustainable wildfire detection
title Neuromorphic solar edge AI for sustainable wildfire detection
spellingShingle Neuromorphic solar edge AI for sustainable wildfire detection
Parada, R
Edge AI
Neuromorphic computing
Solar-Powered drones
Wildfire detection
Internet of robotic things
Sustainable monitoring
title_short Neuromorphic solar edge AI for sustainable wildfire detection
title_full Neuromorphic solar edge AI for sustainable wildfire detection
title_fullStr Neuromorphic solar edge AI for sustainable wildfire detection
title_full_unstemmed Neuromorphic solar edge AI for sustainable wildfire detection
title_sort Neuromorphic solar edge AI for sustainable wildfire detection
dc.creator.none.fl_str_mv Parada, R
author Parada, R
author_facet Parada, R
author_role author
dc.subject.none.fl_str_mv Edge AI
Neuromorphic computing
Solar-Powered drones
Wildfire detection
Internet of robotic things
Sustainable monitoring
topic Edge AI
Neuromorphic computing
Solar-Powered drones
Wildfire detection
Internet of robotic things
Sustainable monitoring
description This paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and BrainChip Akida, integrated into solar-optimized eBee X drones. Results show that the BrainChip Akida achieves 4200 patrol hrs per yr, nearly three times that of traditional CPU systems, while maintaining 87 % solar energy autonomy. The Google Coral TPU and Raspberry Pi 4 reach 66 % and 52 % autonomy, respectively. Fleet scaling analysis demonstrates that increasing drone count from one to eight reduces median wildfire detection time from 18 to 2.2 hrs, surpassing critical response thresholds. Seasonal analysis reveals Akida-based systems can operate fully on solar energy during summer and most of spring and fall, minimizing grid dependency. These findings establish neuromorphic computing as a foundational technology for sustainable, perpetual environmental monitoring within the Internet of Robotic Things (IoRT).
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8878
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8878
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
2027-03-01
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv 2027-03-01
dc.publisher.none.fl_str_mv ELSEVIER
publisher.none.fl_str_mv ELSEVIER
dc.source.none.fl_str_mv Internet of Things
ISSN: 25431536
ISSNe: 25426605
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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