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.

Aggregate graph statistics

Audrito, Giorgio;Damiani, Ferruccio;
2018-01-01

Abstract

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.
2018
1st Workshop on Architectures, Languages and Paradigms for IoT, ALP4IoT 2017
ita
2017
264
18
22
http://published.eptcs.org/
Software
Audrito, Giorgio; Damiani, Ferruccio; Viroli, Mirko
File in questo prodotto:
File Dimensione Formato  
ALP4IoT-EPTCS-2018-Audrito-et-al.pdf

Accesso aperto

Descrizione: Ariticolo principale (workshop)
Tipo di file: PDF EDITORIALE
Dimensione 157.54 kB
Formato Adobe PDF
157.54 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1671332
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact