@inproceedings{NugrohoSchindelhauerChrist2022,
  author    = {Saptadi Nugroho and Christian Schindelhauer and Andreas Christ},
  title     = {Approximate Time-Series Data Aggregation Using Grouping Nodes in Peer to Peer Network},
  series = {Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection},
  volume    = {CCIS 1678},
  editor    = {Alfonso Gonz{\´a}lez-Briones and Ana Almeida and Alberto Fernandez and Alia El Bolock and Dalila Dur{\~a}es and Jaume Jord{\´a}n and Fernando Lopes},
  edition   = {1.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-18696-7 (Print)},
  issn      = {978-3-031-18697-4 (eBook)},
  doi       = {10.1007/978-3-031-18697-4\_25},
  pages     = {306 -- 312},
  year      = {2022},
  abstract  = {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 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).},
  language  = {en}
}