@inproceedings{NugrohoWeinmannSchindelhaueretal.2020,
author = {Saptadi Nugroho and Alexander Weinmann and Christian Schindelhauer and Andreas Christ},
title = {Averaging Emulated Time-Series Data Using Approximate Histograms in Peer to Peer Networks},
series = {Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness},
editor = {Fernando De La Prieta},
publisher = {Springer},
address = {Cham},
isbn = {978-3-030-51998-8 (Print)},
issn = {1865-0929 (Print)},
doi = {10.1007/978-3-030-51999-5\_28},
pages = {339 -- 346},
year = {2020},
abstract = {The interaction between agents in multiagent-based control systems requires peer to peer communication between agents avoiding central control. The sensor nodes represent agents and produce measurement data every time step. The nodes exchange time series data by using the peer to peer network in order to calculate an aggregation function for solving a problem cooperatively. We investigate the aggregation process of averaging data for time series data of nodes in a peer to peer network by using the grouping algorithm of Cichon et al. 2018. Nodes communicate whether data is new and map data values according to their sizes into a histogram. This map message consists of the subintervals and vectors for estimating the node joining and leaving the subinterval. At each time step, the nodes communicate with each other in synchronous rounds to exchange map messages until the network converges to a common map message. The node calculates the average value of time series data produced by all nodes in the network by using the histogram algorithm. The relative error for comparing the output of averaging time series data, and the ground truth of the average value in the network will decrease as the size of the network increases. We perform simulations which show that the approximate histograms method provides a reasonable approximation of time series data.},
language = {en}
}