2018/02/06 by Giorgio Audrito, Ferruccio Damiani, Mirko Viroli
Computer Science · #cs.DC
paper · pdf · doi:10.4204/eptcs.264.2
published as EPTCS 264, 2018, pp. 18-22 · In Proceedings ALP4IoT 2017, arXiv:1802.00976
arxiv created 2018/02/06 · arxiv updated 2018/02/07
Collecting statistic from graph-based data is an increasingly studied topic in the data mining community. We argue that these statistics have great value as well in dynamic IoT contexts: they can support complex computational activities involving distributed coordination and provision of situation recognition. We show that the HyperANF algorithm for calculating the neighbourhood function of vertices of a graph naturally allows for a fully distributed and asynchronous implementation, thanks to a mapping to the field calculus, a distribution model proposed for collective adaptive systems. This mapping gives evidence that the field calculus framework is well-suited to accommodate massively parallel computations over graphs. Furthermore, it provides a new "self-stabilising" building block which can be used in aggregate computing in several contexts, there including improved leader election or network vulnerabilities detection.