FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3206103164> ?p ?o ?g. }

• W3206103164 endingPage "5151" @default.
• W3206103164 startingPage "5140" @default.
• W3206103164 abstract "Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consumed for sensing and communications. The proposed approach uses a long short-term memory (LSTM) model that learns spatiotemporal patterns in sequences of sensorial data for future predictions. The LSTM model and the sensors collaboratively monitor the environment. They are controlled by a reinforcement learning (RL) agent that dynamically decides about using the LSTM prediction versus physical sensing in a way that maximizes energy saving while maintaining prediction accuracy. Two approaches are used for the RL: 1) the Markov decision process (MDP) model-based for low scale applications and 2) deep <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${Q}$ </tex-math></inline-formula> -Network-based for larger scales. Compared to the current literature, the proposed solution is unique in predicting all sensor readings for real-time event detection and providing a model capable of learning long-term spatiotemporal correlations, enabling power conservation and detection accuracy balance. We compare the proposed solutions to the most relevant state-of-the-art approaches using a large real dataset collected in a dynamic space by measuring the accuracy, consumed energy, network lifetime, latency, and missed events’ ratio. To investigate the scalability of the solutions, these parameters are calculated for different network sizes. The results show that the system achieves 50% accuracy with 32% of activation time and 75% accuracy with 60% activation time." @default.
• W3206103164 created "2021-10-25" @default.
• W3206103164 creator A5020500091 @default.
• W3206103164 creator A5066157144 @default.
• W3206103164 creator A5068835268 @default.
• W3206103164 date "2022-08-01" @default.
• W3206103164 modified "2023-10-18" @default.
• W3206103164 title "On Predicting Sensor Readings With Sequence Modeling and Reinforcement Learning for Energy-Efficient IoT Applications" @default.
• W3206103164 cites W1584308190 @default.
• W3206103164 cites W1986759747 @default.
• W3206103164 cites W1991133427 @default.
• W3206103164 cites W1992775328 @default.
• W3206103164 cites W2026063550 @default.
• W3206103164 cites W2033682954 @default.
• W3206103164 cites W2063939750 @default.
• W3206103164 cites W2064675550 @default.
• W3206103164 cites W2067757953 @default.
• W3206103164 cites W2097995023 @default.
• W3206103164 cites W2108241642 @default.
• W3206103164 cites W2124067300 @default.
• W3206103164 cites W2145339207 @default.
• W3206103164 cites W2184221303 @default.
• W3206103164 cites W2568808500 @default.
• W3206103164 cites W2575705757 @default.
• W3206103164 cites W2614034121 @default.
• W3206103164 cites W2746816686 @default.
• W3206103164 cites W2763430085 @default.
• W3206103164 cites W2766218248 @default.
• W3206103164 cites W2803231950 @default.
• W3206103164 cites W2803889994 @default.
• W3206103164 cites W2884534163 @default.
• W3206103164 cites W2897947308 @default.
• W3206103164 cites W2899532358 @default.
• W3206103164 cites W2903350505 @default.
• W3206103164 cites W2908288717 @default.
• W3206103164 cites W2909545524 @default.
• W3206103164 cites W2921041718 @default.
• W3206103164 cites W2946628591 @default.
• W3206103164 cites W2950718835 @default.
• W3206103164 cites W3026290994 @default.
• W3206103164 cites W3134318045 @default.
• W3206103164 cites W3139245532 @default.
• W3206103164 cites W3157577121 @default.
• W3206103164 cites W3176184800 @default.
• W3206103164 doi "https://doi.org/10.1109/tsmc.2021.3116141" @default.
• W3206103164 hasPublicationYear "2022" @default.
• W3206103164 type Work @default.
• W3206103164 sameAs 3206103164 @default.
• W3206103164 citedByCount "2" @default.
• W3206103164 countsByYear W32061031642022 @default.
• W3206103164 crossrefType "journal-article" @default.
• W3206103164 hasAuthorship W3206103164A5020500091 @default.
• W3206103164 hasAuthorship W3206103164A5066157144 @default.
• W3206103164 hasAuthorship W3206103164A5068835268 @default.
• W3206103164 hasConcept C105795698 @default.
• W3206103164 hasConcept C106189395 @default.
• W3206103164 hasConcept C119857082 @default.
• W3206103164 hasConcept C121332964 @default.
• W3206103164 hasConcept C149635348 @default.
• W3206103164 hasConcept C154945302 @default.
• W3206103164 hasConcept C159886148 @default.
• W3206103164 hasConcept C186370098 @default.
• W3206103164 hasConcept C24590314 @default.
• W3206103164 hasConcept C2778112365 @default.
• W3206103164 hasConcept C2779662365 @default.
• W3206103164 hasConcept C31258907 @default.
• W3206103164 hasConcept C33923547 @default.
• W3206103164 hasConcept C41008148 @default.
• W3206103164 hasConcept C46637626 @default.
• W3206103164 hasConcept C48044578 @default.
• W3206103164 hasConcept C54355233 @default.
• W3206103164 hasConcept C62520636 @default.
• W3206103164 hasConcept C76155785 @default.
• W3206103164 hasConcept C77088390 @default.
• W3206103164 hasConcept C79403827 @default.
• W3206103164 hasConcept C81860439 @default.
• W3206103164 hasConcept C82876162 @default.
• W3206103164 hasConcept C86803240 @default.
• W3206103164 hasConcept C97541855 @default.
• W3206103164 hasConceptScore W3206103164C105795698 @default.
• W3206103164 hasConceptScore W3206103164C106189395 @default.
• W3206103164 hasConceptScore W3206103164C119857082 @default.
• W3206103164 hasConceptScore W3206103164C121332964 @default.
• W3206103164 hasConceptScore W3206103164C149635348 @default.
• W3206103164 hasConceptScore W3206103164C154945302 @default.
• W3206103164 hasConceptScore W3206103164C159886148 @default.
• W3206103164 hasConceptScore W3206103164C186370098 @default.
• W3206103164 hasConceptScore W3206103164C24590314 @default.
• W3206103164 hasConceptScore W3206103164C2778112365 @default.
• W3206103164 hasConceptScore W3206103164C2779662365 @default.
• W3206103164 hasConceptScore W3206103164C31258907 @default.
• W3206103164 hasConceptScore W3206103164C33923547 @default.
• W3206103164 hasConceptScore W3206103164C41008148 @default.
• W3206103164 hasConceptScore W3206103164C46637626 @default.
• W3206103164 hasConceptScore W3206103164C48044578 @default.
• W3206103164 hasConceptScore W3206103164C54355233 @default.
• W3206103164 hasConceptScore W3206103164C62520636 @default.
• W3206103164 hasConceptScore W3206103164C76155785 @default.

• next