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.

翻译：在协调系统中，一项反复出现的任务是管理（估计、预测或控制）随空间变化的信号，例如分布式感知数据或计算结果。尤其在大规模场景中，该问题可通过去中心化与情境感知计算系统加以解决：节点可在本地感知、处理信号并对其采取行动，同时与邻居节点协作以实施集体策略。基于此，本文设计了一种通过协作自适应采样来估计空间现象的分布式协调策略。我们的方法基于动态划分空间区域的思想，这些区域通过竞争与扩张/收缩来提供精确的聚合采样。此类区域因而定义了一种"流体"式的虚拟空间——其结构会随底层现象施加的压力动态调整。我们在基于场的协调框架下提出了一种自适应采样算法，并证明其具有自稳定性和局部最优性。最后，通过仿真验证了该算法在维持精度与效率间可调权衡的同时，能够有效实现空间自适应采样。