Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385759362> ?p ?o ?g. }
- W4385759362 endingPage "1200" @default.
- W4385759362 startingPage "1200" @default.
- W4385759362 abstract "Information aggregation in distributed sensor networks has received significant attention from researchers in various disciplines. Distributed consensus algorithms are broadly developed to accelerate the convergence to consensus under different communication and/or energy limitations. Non-Bayesian social learning strategies are representative algorithms for distributed agents to learn progressively an underlying state of nature by information communications and evolutions. This work designs a new non-Bayesian social learning strategy named the hypergraph social learning by introducing the higher-order topology as the underlying communication network structure, with its convergence as well as the convergence rate theoretically analyzed. Extensive numerical examples are provided to demonstrate the effectiveness of the framework and reveal its superior performance when applying to sensor networks in tasks such as cooperative positioning. The designed framework can assist sensor network designers to develop more efficient communication topology, which can better resist environmental obstructions, and also has theoretical and applied values in broad areas such as distributed parameter estimation, dispersed information aggregation and social networks." @default.
- W4385759362 created "2023-08-12" @default.
- W4385759362 creator A5003378348 @default.
- W4385759362 creator A5008051662 @default.
- W4385759362 creator A5026080557 @default.
- W4385759362 creator A5069580938 @default.
- W4385759362 date "2023-08-11" @default.
- W4385759362 modified "2023-09-26" @default.
- W4385759362 title "Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology" @default.
- W4385759362 cites W1978009662 @default.
- W4385759362 cites W1984661256 @default.
- W4385759362 cites W2033041968 @default.
- W4385759362 cites W2058105398 @default.
- W4385759362 cites W2058360748 @default.
- W4385759362 cites W2069138607 @default.
- W4385759362 cites W2084990575 @default.
- W4385759362 cites W2107396783 @default.
- W4385759362 cites W2108299551 @default.
- W4385759362 cites W2108306501 @default.
- W4385759362 cites W2111857756 @default.
- W4385759362 cites W2117208220 @default.
- W4385759362 cites W2133380705 @default.
- W4385759362 cites W2134931138 @default.
- W4385759362 cites W2140950724 @default.
- W4385759362 cites W2144052787 @default.
- W4385759362 cites W2145976030 @default.
- W4385759362 cites W2149259182 @default.
- W4385759362 cites W2155723880 @default.
- W4385759362 cites W2162658997 @default.
- W4385759362 cites W2169826194 @default.
- W4385759362 cites W2481949923 @default.
- W4385759362 cites W2737034463 @default.
- W4385759362 cites W2748936567 @default.
- W4385759362 cites W2767674461 @default.
- W4385759362 cites W2795239532 @default.
- W4385759362 cites W2911850641 @default.
- W4385759362 cites W2924173387 @default.
- W4385759362 cites W2962743217 @default.
- W4385759362 cites W2963118811 @default.
- W4385759362 cites W2963881501 @default.
- W4385759362 cites W2973904754 @default.
- W4385759362 cites W3011951066 @default.
- W4385759362 cites W3018692113 @default.
- W4385759362 cites W3083024126 @default.
- W4385759362 cites W3102646983 @default.
- W4385759362 cites W3122000667 @default.
- W4385759362 cites W3157532984 @default.
- W4385759362 cites W3179235957 @default.
- W4385759362 cites W4220762598 @default.
- W4385759362 cites W4281784631 @default.
- W4385759362 cites W4312240205 @default.
- W4385759362 cites W4312735666 @default.
- W4385759362 cites W4312794301 @default.
- W4385759362 cites W4313008092 @default.
- W4385759362 cites W4323923320 @default.
- W4385759362 cites W4327714829 @default.
- W4385759362 cites W4379378301 @default.
- W4385759362 cites W4379471157 @default.
- W4385759362 cites W4380873324 @default.
- W4385759362 doi "https://doi.org/10.3390/e25081200" @default.
- W4385759362 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37628230" @default.
- W4385759362 hasPublicationYear "2023" @default.
- W4385759362 type Work @default.
- W4385759362 citedByCount "0" @default.
- W4385759362 crossrefType "journal-article" @default.
- W4385759362 hasAuthorship W4385759362A5003378348 @default.
- W4385759362 hasAuthorship W4385759362A5008051662 @default.
- W4385759362 hasAuthorship W4385759362A5026080557 @default.
- W4385759362 hasAuthorship W4385759362A5069580938 @default.
- W4385759362 hasBestOaLocation W43857593621 @default.
- W4385759362 hasConcept C11413529 @default.
- W4385759362 hasConcept C114614502 @default.
- W4385759362 hasConcept C118615104 @default.
- W4385759362 hasConcept C120314980 @default.
- W4385759362 hasConcept C127162648 @default.
- W4385759362 hasConcept C130120984 @default.
- W4385759362 hasConcept C15744967 @default.
- W4385759362 hasConcept C162324750 @default.
- W4385759362 hasConcept C184720557 @default.
- W4385759362 hasConcept C19417346 @default.
- W4385759362 hasConcept C199845137 @default.
- W4385759362 hasConcept C24590314 @default.
- W4385759362 hasConcept C2777303404 @default.
- W4385759362 hasConcept C2779582901 @default.
- W4385759362 hasConcept C2781221856 @default.
- W4385759362 hasConcept C31258907 @default.
- W4385759362 hasConcept C33923547 @default.
- W4385759362 hasConcept C41008148 @default.
- W4385759362 hasConcept C50522688 @default.
- W4385759362 hasConcept C57869625 @default.
- W4385759362 hasConceptScore W4385759362C11413529 @default.
- W4385759362 hasConceptScore W4385759362C114614502 @default.
- W4385759362 hasConceptScore W4385759362C118615104 @default.
- W4385759362 hasConceptScore W4385759362C120314980 @default.
- W4385759362 hasConceptScore W4385759362C127162648 @default.
- W4385759362 hasConceptScore W4385759362C130120984 @default.
- W4385759362 hasConceptScore W4385759362C15744967 @default.
- W4385759362 hasConceptScore W4385759362C162324750 @default.