Federated learning for 5G-enabled infrastructure inspection with UAVs
Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctionin...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-022-00254-z |
| _version_ | 1797977180907503616 |
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| author | Alexios Lekidis |
| author_facet | Alexios Lekidis |
| author_sort | Alexios Lekidis |
| collection | DOAJ |
| description | Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation’s Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation. |
| first_indexed | 2024-04-11T05:03:56Z |
| format | Article |
| id | doaj.art-2d2609f6a0b34ed3bb35e8d6cdaefccc |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-04-11T05:03:56Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-2d2609f6a0b34ed3bb35e8d6cdaefccc2022-12-25T12:31:19ZengSpringerOpenEnergy Informatics2520-89422022-12-015111310.1186/s42162-022-00254-zFederated learning for 5G-enabled infrastructure inspection with UAVsAlexios Lekidis0Public Power Corporation S.A.Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation’s Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation.https://doi.org/10.1186/s42162-022-00254-zElectricity infrastructure inspectionMulti-access edge computingFederated learningUnmanned aerial vehicles5G NFVNetwork slicing |
| spellingShingle | Alexios Lekidis Federated learning for 5G-enabled infrastructure inspection with UAVs Energy Informatics Electricity infrastructure inspection Multi-access edge computing Federated learning Unmanned aerial vehicles 5G NFV Network slicing |
| title | Federated learning for 5G-enabled infrastructure inspection with UAVs |
| title_full | Federated learning for 5G-enabled infrastructure inspection with UAVs |
| title_fullStr | Federated learning for 5G-enabled infrastructure inspection with UAVs |
| title_full_unstemmed | Federated learning for 5G-enabled infrastructure inspection with UAVs |
| title_short | Federated learning for 5G-enabled infrastructure inspection with UAVs |
| title_sort | federated learning for 5g enabled infrastructure inspection with uavs |
| topic | Electricity infrastructure inspection Multi-access edge computing Federated learning Unmanned aerial vehicles 5G NFV Network slicing |
| url | https://doi.org/10.1186/s42162-022-00254-z |
| work_keys_str_mv | AT alexioslekidis federatedlearningfor5genabledinfrastructureinspectionwithuavs |