FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308103430> ?p ?o ?g. }

• W4308103430 endingPage "102685" @default.
• W4308103430 startingPage "102685" @default.
• W4308103430 abstract "Internet-of-Things (IoT)-based cyber–physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber–physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization." @default.
• W4308103430 created "2022-11-08" @default.
• W4308103430 creator A5050702557 @default.
• W4308103430 creator A5053118726 @default.
• W4308103430 creator A5061679058 @default.
• W4308103430 creator A5088989835 @default.
• W4308103430 date "2023-01-01" @default.
• W4308103430 modified "2023-09-25" @default.
• W4308103430 title "An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems" @default.
• W4308103430 cites W1837982953 @default.
• W4308103430 cites W2014574262 @default.
• W4308103430 cites W2125932763 @default.
• W4308103430 cites W2262390119 @default.
• W4308103430 cites W2306447822 @default.
• W4308103430 cites W2345393872 @default.
• W4308103430 cites W2416799949 @default.
• W4308103430 cites W2527484278 @default.
• W4308103430 cites W2541133059 @default.
• W4308103430 cites W2559872199 @default.
• W4308103430 cites W2917849969 @default.
• W4308103430 cites W2967459865 @default.
• W4308103430 cites W3098769338 @default.
• W4308103430 cites W3122864121 @default.
• W4308103430 cites W4205302404 @default.
• W4308103430 cites W4205619814 @default.
• W4308103430 cites W4221012128 @default.
• W4308103430 cites W4293155325 @default.
• W4308103430 cites W4313189329 @default.
• W4308103430 doi "https://doi.org/10.1016/j.simpat.2022.102685" @default.
• W4308103430 hasPublicationYear "2023" @default.
• W4308103430 type Work @default.
• W4308103430 citedByCount "4" @default.
• W4308103430 countsByYear W43081034302023 @default.
• W4308103430 crossrefType "journal-article" @default.
• W4308103430 hasAuthorship W4308103430A5050702557 @default.
• W4308103430 hasAuthorship W4308103430A5053118726 @default.
• W4308103430 hasAuthorship W4308103430A5061679058 @default.
• W4308103430 hasAuthorship W4308103430A5088989835 @default.
• W4308103430 hasBestOaLocation W43081034301 @default.
• W4308103430 hasConcept C111919701 @default.
• W4308103430 hasConcept C119857082 @default.
• W4308103430 hasConcept C120314980 @default.
• W4308103430 hasConcept C124101348 @default.
• W4308103430 hasConcept C127413603 @default.
• W4308103430 hasConcept C138236772 @default.
• W4308103430 hasConcept C144024400 @default.
• W4308103430 hasConcept C149923435 @default.
• W4308103430 hasConcept C154945302 @default.
• W4308103430 hasConcept C162307627 @default.
• W4308103430 hasConcept C179768478 @default.
• W4308103430 hasConcept C201995342 @default.
• W4308103430 hasConcept C2778456923 @default.
• W4308103430 hasConcept C2780451532 @default.
• W4308103430 hasConcept C2908647359 @default.
• W4308103430 hasConcept C41008148 @default.
• W4308103430 hasConcept C75684735 @default.
• W4308103430 hasConcept C79974875 @default.
• W4308103430 hasConcept C98045186 @default.
• W4308103430 hasConceptScore W4308103430C111919701 @default.
• W4308103430 hasConceptScore W4308103430C119857082 @default.
• W4308103430 hasConceptScore W4308103430C120314980 @default.
• W4308103430 hasConceptScore W4308103430C124101348 @default.
• W4308103430 hasConceptScore W4308103430C127413603 @default.
• W4308103430 hasConceptScore W4308103430C138236772 @default.
• W4308103430 hasConceptScore W4308103430C144024400 @default.
• W4308103430 hasConceptScore W4308103430C149923435 @default.
• W4308103430 hasConceptScore W4308103430C154945302 @default.
• W4308103430 hasConceptScore W4308103430C162307627 @default.
• W4308103430 hasConceptScore W4308103430C179768478 @default.
• W4308103430 hasConceptScore W4308103430C201995342 @default.
• W4308103430 hasConceptScore W4308103430C2778456923 @default.
• W4308103430 hasConceptScore W4308103430C2780451532 @default.
• W4308103430 hasConceptScore W4308103430C2908647359 @default.
• W4308103430 hasConceptScore W4308103430C41008148 @default.
• W4308103430 hasConceptScore W4308103430C75684735 @default.
• W4308103430 hasConceptScore W4308103430C79974875 @default.
• W4308103430 hasConceptScore W4308103430C98045186 @default.
• W4308103430 hasLocation W43081034301 @default.
• W4308103430 hasOpenAccess W4308103430 @default.
• W4308103430 hasPrimaryLocation W43081034301 @default.
• W4308103430 hasRelatedWork W2804912624 @default.
• W4308103430 hasRelatedWork W2889972190 @default.
• W4308103430 hasRelatedWork W2945616868 @default.
• W4308103430 hasRelatedWork W2990230885 @default.
• W4308103430 hasRelatedWork W2995654207 @default.
• W4308103430 hasRelatedWork W3211931762 @default.
• W4308103430 hasRelatedWork W4200161860 @default.
• W4308103430 hasRelatedWork W4223535265 @default.
• W4308103430 hasRelatedWork W4226427977 @default.
• W4308103430 hasRelatedWork W4316660948 @default.
• W4308103430 hasVolume "122" @default.
• W4308103430 isParatext "false" @default.
• W4308103430 isRetracted "false" @default.
• W4308103430 workType "article" @default.