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Self-Adaptation to Device Distribution in the Internet of Things
Journal article   Peer reviewed

Self-Adaptation to Device Distribution in the Internet of Things

Jacob Beal, Mirko Viroli, Danilo Pianini and Ferruccio Damiani
ACM transactions on autonomous and adaptive systems, Vol.12(3), pp.1-29
10/09/2017
DOI: 10.1145/3105758
url
http://hdl.handle.net/2318/1649733View
Open Access

Abstract

A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which the devices are situated rather than the particulars of how devices happen to be distributed through it. In this article, we identify a new property of distributed algorithms, eventual consistency, which guarantees that computation converges to a final state that approximates a predictable limit, based on the continuous environment, as the density and speed of devices increases. We then identify a large class of programs that are eventually consistent, building on prior results on the field calculus computational model (Beal et al. 2015; Viroli et al. 2015a) that identify a class of self-stabilizing programs. Finally, we confirm through simulation of IoT application scenarios that eventually consistent programs from this class can provide resilient behavior where programs that are only converging fail badly.
 Computer Science Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Science & Technology Technology

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