Space-Fluid Adaptive Sampling by Self-Organisation

TL;DR

This paper introduces a decentralized, adaptive sampling method for spatial signals, where regions dynamically compete and adapt to efficiently estimate phenomena in large-scale distributed systems.

Contribution

It presents a novel fluid-like, self-stabilizing algorithm for adaptive spatial sampling that optimizes accuracy and efficiency in decentralized settings.

Findings

Algorithm is self-stabilizing and locally optimal.

Simulation confirms effective spatially adaptive sampling.

Trade-off between accuracy and efficiency is tunable.

Abstract

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is "fluid", since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework, and prove it is self-stabilising and locally optimal. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling while maintaining a tuneable trade-off between accuracy and efficiency.