Aggregate centrality measures for IoT-based coordination

Abstract

• We provide a natural translation of centrality measure algorithms in field calculus. • This translation is self-stabilising and distributed. • We investigate applications of centrality measures in IoT/edge scenarios. • We validate potential applicability in two simulated scenarios. Collecting statistics from graph-based data is an increasingly studied topic in the data mining community. We argue that they can have great value in the coordination of dynamic IoT systems as well, especially to support complex coordination strategies related to distributed situation recognition. Thanks to a mapping to the field calculus, a distribution coordination model proposed for collective adaptive systems, we show that many existing “centrality measures” for graphs can be naturally turned into field computations that compute the centrality of nodes in a network. Not only this mapping gives evidence that the field coordination is well-suited to accommodate massively parallel computations over graphs, but also it provides a new basic “brick” of coordination which can be used in several contexts, there including improved leader election or network vulnerabilities detection. We validate our findings by simulation, first measuring the ability of the translated algorithm to self-adjust to network changes, then investigating an application of centrality measures for data summarisation.