{"authors":[{"id":null,"fullName":"Garcia Boris, Marçal","name":"Marçal","surname":"Garcia Boris","rank":1,"pid":null},{"id":null,"fullName":"Oliveras Torra, Joan","name":"Joan","surname":"Oliveras Torra","rank":2,"pid":null},{"id":null,"fullName":"Aguilera Luzón, David","name":"David","surname":"Aguilera Luzón","rank":3,"pid":null},{"id":null,"fullName":"Liu, Peini","name":"Peini","surname":"Liu","rank":4,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-0058-8732"},"provenance":null}},{"id":null,"fullName":"Divan Koller, Mario José","name":"Mario José","surname":"Divan Koller","rank":5,"pid":null},{"id":"orcid_______::f6bca9e4d29f530a59aa4b31a8f6d994","fullName":"Berral García, Josep Lluís","name":"Josep Lluís","surname":"Berral García","rank":6,"pid":{"id":{"scheme":"orcid","value":"0000-0003-3037-3580"},"provenance":null}}],"openAccessColor":null,"publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"und","label":"Undetermined"},"countries":[{"code":"ES","label":"Spain","provenance":null}],"subjects":[{"subject":{"scheme":"keyword","value":"Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"keyword","value":"Video analytics"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Edge computing"},"provenance":null},{"subject":{"scheme":"keyword","value":"Environmental stress"},"provenance":null},{"subject":{"scheme":"keyword","value":"Reliability"},"provenance":null},{"subject":{"scheme":"keyword","value":"Urban mobility"},"provenance":null}],"mainTitle":"A Reliability Score for Video-Analytics Edge-IoT Devices in Urban Environments","subTitle":null,"descriptions":["Performing video-analytics in Edge and IoT devices towards urban mobility allows computing near-data, providing fast and autonomous management from streets and roads. Thanks to advances in artificial intelligence methods, real-time classification of camera-provided image feeds can be performed in low-power devices to be deployed next to urban signaling and city services. However, devices deployed “in the wild” are affected by environmental factors such as temperature, either ambient or retained by its container, humidity or voltage changes, coming from sensorized urban furniture. Deciding where to deploy video-analytics workload requires a minimal device performance reliability, assuring that load can be processed in-place, before moving it towards the Fog/Cloud or other nearby devices. Here we present a simplistic factorization of a reliability Score to be quickly computed in Edge nodes, towards orchestrating workload according to the node surrounding environment. The score takes into account ambient temperature and humidity, also received voltage. The scoring is evaluated performing video-analytics workloads on real devices under physical stress, observing the performance degradation related to the external and internal factors applied onto the device. From the study, we observe how the different factors affect the functioning mode and quality of service, aside of indicating an estimation of the reliability of the urban-placed devices.","This work is financed by the INTEL UFunding #14780, the EU-HORIZON and MSCA programmes under GA.101092646 and GA.101086248, by Generalitat de Catalunya (AGAUR) GA.2021-SGR-00478, and the Spanish Ministry of Science (MICINN), the Research State Agency (AEI) and European Regional Development Funds (ERDF/FEDER) PID2021-126248OB-I00, MCIN/AEI/10.13039/ 501100011033/FEDER, UE.","Peer Reviewed"],"publicationDate":"2025-06-09","publisher":"IEEE","embargoEndDate":null,"sources":["Crossref","reponame:UPCommons. 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