A Survey of Distributed Data Aggregation Algorithms

TL;DR

This survey reviews various distributed data aggregation algorithms, categorizing their techniques, defining aggregation concepts, and providing guidelines for selecting appropriate methods based on their characteristics.

Contribution

It offers a formal definition of aggregation, organizes existing algorithms into a taxonomy, and provides practical guidelines for their selection and application.

Findings

Classifies aggregation functions and techniques

Organizes algorithms into a comprehensive taxonomy

Provides guidelines for choosing suitable aggregation methods

Abstract

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine 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.