A Survey of Distributed Data Aggregation Algorithms


Distributed knowledge aggregation is a vital task, permitting the decentralized determination of meaningful global properties, which will then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like Count, Add, and Average. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and several others. In the last decade, many completely different approaches are proposed, with totally different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable quantity and variety of aggregation algorithms, it will be troublesome and time consuming to see that techniques can be more appropriate to use in specific settings, justifying the existence of a survey to help during this task. This work reviews the cutting-edge on distributed knowledge aggregation algorithms, providing three main contributions. Initial, it formally defines the concept of aggregation, characterizing the various types of aggregation functions. Second, it succinctly describes the most aggregation techniques, organizing them in a very taxonomy. Finally, it provides some tips toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

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