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Approximate Time-Series Data Aggregation Using Grouping Nodes in Peer to Peer Network

  • We consider the local group of agents for exchanging the time-series data value and computing the approximation of the mean value of all agents. An agent represented by a node knows all local neighbor nodes in the same group. The node has the contact information of other nodes in other groups. The nodes interact with each other in synchronous rounds to exchange the updated time-series data valueWe consider the local group of agents for exchanging the time-series data value and computing the approximation of the mean value of all agents. An agent represented by a node knows all local neighbor nodes in the same group. The node has the contact information of other nodes in other groups. The nodes interact with each other in synchronous rounds to exchange the updated time-series data value using the random call communication model. The amount of data exchanged between agent-based sensors in the local group network affects the accuracy of the aggregation function results. At each time step, the agent-based sensor can update the input data value and send the updated data value to the group head node. The group head node sends the updated data value to all group members in the same group. Grouping nodes in peer-to-peer networks show an improvement in Mean Squared Error (MSE).show moreshow less

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Metadaten
Document Type:Conference Proceeding
Title (English):Approximate Time-Series Data Aggregation Using Grouping Nodes in Peer to Peer Network
Author:Saptadi Nugroho, Christian Schindelhauer, Andreas ChristORCiDGND
Edition:1.
Year of Publication:2022
Date of first Publication:2022/10/13
Place of publication:Cham
Publisher:Springer
First Page:306
Last Page:312
Parent Title (English):Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection
Editor:Alfonso González-Briones, Ana Almeida, Alberto Fernandez, Alia El Bolock, Dalila Durães, Jaume Jordán, Fernando Lopes
Volume:CCIS 1678
ISBN:978-3-031-18696-7 (Print)
ISSN:978-3-031-18697-4 (eBook)
ISSN:1865-0929 (Print)
ISSN:1865-0937 (eBook)
DOI:https://doi.org/10.1007/978-3-031-18697-4_25
URL:https://link.springer.com/chapter/10.1007/978-3-031-18697-4_25
Language:English
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Tag:Agent based sensor; Approximation; Peer to peer network; Random call model; Time series data
Open Access: Closed 
Relevance:Konferenzbeitrag: h5-Index < 30
Licence (German):License LogoUrheberrechtlich geschützt
Comment:
Konferenz: International Workshops of PAAMS 2022, July 13-15, 2022, L'Aquila, Italy