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A Survey of Distributed Data Aggregation Algorithms
Abstract:
Distributed data aggregation is an important task, allowing the
decentralized determination of meaningful global properties, which can
then be used to direct the execution of other applications. The resulting
values are derived by the distributed computation of functions
like Count, Sum, and Average. Some application examples deal with the
determination of the network size, total storage capacity, average load,
majorities and many others. In the last decade, many different approaches
have been proposed, with different trade-offs in terms of accuracy,
reliability, message and time complexity. Due to the considerable amount
and variety of aggregation algorithms, it can be difficult and time
consuming to determine which techniques will be more appropriate to use
in specific settings, justifying the existence of a survey to aid in this task.
This work reviews the state of the art on distributed data aggregation
algorithms, providing three main contributions. First, it formally defines
the concept of aggregation, characterizing the different types of
aggregation functions. Second, it succinctly describes the main aggregation
techniques, organizing them in a taxonomy. Finally, it provides some
guidelines toward the selection and use of the most relevant techniques,
summarizing their principal characteristics.

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A survey of distributed data aggregation algorithms

  • 1. A Survey of Distributed Data Aggregation Algorithms Abstract: Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like Count, Sum, and Average. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.