FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313146764> ?p ?o ?g. }

• W4313146764 endingPage "2573" @default.
• W4313146764 startingPage "2557" @default.
• W4313146764 abstract "Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT) networks. Information freshness in data collection can be characterized by the Age of Information (AoI). It is highly challenging to schedule multiple energy-constrained UAVs to improve information freshness especially when the generation instants of sensing samples are unpredictable. To deal with this issue, we leverage state-of-art reinforcement learning (RL) methods to design flight trajectories of UAVs without knowing the sampling mode each sensor node (SN) adopts. Each SN can sample the environment at periodical or random intervals. Multiple energy-constrained UAVs are dispatched to collect update packets from the SNs when flying over them. The UAV trajectory planning problem for AoI minimization is formulated as a Markov decision process (MDP). The objective is to minimize the average AoI of the SNs under the constraints of energy capacity and collision avoidance for the UAVs. Then, we propose two learning algorithms based on the Sarsa and value-decomposition network (VDN), respectively, which allow the UAVs to fulfill data collection tasks requested by the SNs. By learning directly from the environment, the Sarsa-based algorithm can approach the optimal policy asymptotically when certain conditions are satisfied. As one of the most popular multiagent deep RL methods, the VDN-based algorithm enables each UAV to make its own decision independently on its flight and data collection based on the partially observed network information. Simulation results validate the effectiveness of the proposed two learning-based algorithms compared with baseline policies." @default.
• W4313146764 created "2023-01-06" @default.
• W4313146764 creator A5002171184 @default.
• W4313146764 creator A5002746327 @default.
• W4313146764 creator A5027155270 @default.
• W4313146764 creator A5038677748 @default.
• W4313146764 creator A5062064525 @default.
• W4313146764 creator A5063691948 @default.
• W4313146764 date "2023-02-01" @default.
• W4313146764 modified "2023-10-17" @default.
• W4313146764 title "Learning-Based Data Gathering for Information Freshness in UAV-Assisted IoT Networks" @default.
• W4313146764 cites W1902748306 @default.
• W4313146764 cites W1993918491 @default.
• W4313146764 cites W2066786775 @default.
• W4313146764 cites W2145339207 @default.
• W4313146764 cites W2150339816 @default.
• W4313146764 cites W2165131254 @default.
• W4313146764 cites W2268751503 @default.
• W4313146764 cites W2604830243 @default.
• W4313146764 cites W2649848418 @default.
• W4313146764 cites W2744248483 @default.
• W4313146764 cites W2807521632 @default.
• W4313146764 cites W2886509985 @default.
• W4313146764 cites W2896835129 @default.
• W4313146764 cites W2908558272 @default.
• W4313146764 cites W2917067164 @default.
• W4313146764 cites W2962141687 @default.
• W4313146764 cites W2962691117 @default.
• W4313146764 cites W2962748258 @default.
• W4313146764 cites W2962804513 @default.
• W4313146764 cites W2963523817 @default.
• W4313146764 cites W2963739418 @default.
• W4313146764 cites W2964018918 @default.
• W4313146764 cites W2964023906 @default.
• W4313146764 cites W2964310887 @default.
• W4313146764 cites W2966659925 @default.
• W4313146764 cites W2996110936 @default.
• W4313146764 cites W3007091509 @default.
• W4313146764 cites W3039515597 @default.
• W4313146764 cites W3045471271 @default.
• W4313146764 cites W3046016465 @default.
• W4313146764 cites W3093571944 @default.
• W4313146764 cites W3098405735 @default.
• W4313146764 cites W3101996971 @default.
• W4313146764 cites W3109039904 @default.
• W4313146764 cites W3111412729 @default.
• W4313146764 cites W3111740206 @default.
• W4313146764 cites W3137144115 @default.
• W4313146764 cites W3171715134 @default.
• W4313146764 cites W3203069471 @default.
• W4313146764 cites W3211189031 @default.
• W4313146764 cites W3214609206 @default.
• W4313146764 cites W4213374851 @default.
• W4313146764 cites W4297310212 @default.
• W4313146764 doi "https://doi.org/10.1109/jiot.2022.3215521" @default.
• W4313146764 hasPublicationYear "2023" @default.
• W4313146764 type Work @default.
• W4313146764 citedByCount "3" @default.
• W4313146764 countsByYear W43131467642023 @default.
• W4313146764 crossrefType "journal-article" @default.
• W4313146764 hasAuthorship W4313146764A5002171184 @default.
• W4313146764 hasAuthorship W4313146764A5002746327 @default.
• W4313146764 hasAuthorship W4313146764A5027155270 @default.
• W4313146764 hasAuthorship W4313146764A5038677748 @default.
• W4313146764 hasAuthorship W4313146764A5062064525 @default.
• W4313146764 hasAuthorship W4313146764A5063691948 @default.
• W4313146764 hasConcept C105795698 @default.
• W4313146764 hasConcept C106189395 @default.
• W4313146764 hasConcept C111919701 @default.
• W4313146764 hasConcept C119857082 @default.
• W4313146764 hasConcept C120314980 @default.
• W4313146764 hasConcept C133462117 @default.
• W4313146764 hasConcept C153083717 @default.
• W4313146764 hasConcept C154945302 @default.
• W4313146764 hasConcept C158379750 @default.
• W4313146764 hasConcept C159886148 @default.
• W4313146764 hasConcept C31258907 @default.
• W4313146764 hasConcept C33923547 @default.
• W4313146764 hasConcept C41008148 @default.
• W4313146764 hasConcept C68387754 @default.
• W4313146764 hasConcept C79403827 @default.
• W4313146764 hasConcept C97541855 @default.
• W4313146764 hasConceptScore W4313146764C105795698 @default.
• W4313146764 hasConceptScore W4313146764C106189395 @default.
• W4313146764 hasConceptScore W4313146764C111919701 @default.
• W4313146764 hasConceptScore W4313146764C119857082 @default.
• W4313146764 hasConceptScore W4313146764C120314980 @default.
• W4313146764 hasConceptScore W4313146764C133462117 @default.
• W4313146764 hasConceptScore W4313146764C153083717 @default.
• W4313146764 hasConceptScore W4313146764C154945302 @default.
• W4313146764 hasConceptScore W4313146764C158379750 @default.
• W4313146764 hasConceptScore W4313146764C159886148 @default.
• W4313146764 hasConceptScore W4313146764C31258907 @default.
• W4313146764 hasConceptScore W4313146764C33923547 @default.
• W4313146764 hasConceptScore W4313146764C41008148 @default.
• W4313146764 hasConceptScore W4313146764C68387754 @default.
• W4313146764 hasConceptScore W4313146764C79403827 @default.
• W4313146764 hasConceptScore W4313146764C97541855 @default.

• next