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.

讨论在分布式计算系统中，如何通过自适应采样算法实现对空间现象的分布式协调估计，以达到对精度和效率进行可调平衡的采样过程。