TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Nugroho, Saptadi
A1  - Weinmann, Alexander
A1  - Schindelhauer, Christian
T1  - Trade off Between Accuracy and Message Complexity for Approximate Data Aggregation
T2  - Proceedings : 18th Annual International Conference on Distributed Computing in Sensor Systems (DCOSS 2022)
N2  - We consider large scale Peer-to-Peer Sensor Networks, which try to calculate and distribute the mean value of all sensor inputs. For this we design, simulate and evaluate distributed approximation algorithms which reduce the number of messages. The main difference of these algorithms is the underlying communication protocol which all use the random call model, where in discrete round model each node can call a random sensor node with uniform probability.The amount of data exchanged between sensor nodes and used in the calculation process affects the accuracy of the aggregation results leading to a trade-off situation. The key idea of our algorithms is to limit the sample size using the Finite Population Correction (FPC) method and collect the data using a distribution aggregation using Push-Pull Sampling, Pull Sampling, and Push Sampling communication protocols. It turns out that all methods show exponential improvement of Mean Squared Error (MSE) with the number of messages and rounds.
KW  - data aggregation
KW  - sensor network
KW  - message complexity
KW  - approximation
KW  - random call model
Y1  - 2022
UR  - https://ieeexplore.ieee.org/document/9881662
SN  - 2325-2944 (Elektronisch)
SS  - 2325-2944 (Elektronisch)
SN  - 2325-2936 (Print on Demand)
SS  - 2325-2936 (Print on Demand)
SN  - 978-1-6654-9512-7 (Elektronisch)
SB  - 978-1-6654-9512-7 (Elektronisch)
SN  - 978-1-6654-9513-4 (Print on Demand)
SB  - 978-1-6654-9513-4 (Print on Demand)
U6  - https://doi.org/10.1109/DCOSS54816.2022.00021
DO  - https://doi.org/10.1109/DCOSS54816.2022.00021
SP  - 61
EP  - 64
PB  - IEEE
ER  -