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:协调系统的一项经常性任务是管理(估计、预测或控制)空间不同信号,例如分布式感知数据或计算结果。特别是在大规模环境下,问题可以通过分散和定位的计算机系统加以解决:节点可以局部感知、处理和根据信号采取行动,并与邻国协调以实施集体战略。因此,在这项工作中,我们设计了通过协作性适应抽样估计空间现象的协调战略。我们的设计基于将空间动态分割到竞争和增长/缩小以提供准确的汇总抽样的区域的想法。因此,这些区域定义了一种虚拟化的空间,即“浮”空间,因为其结构适应基本现象造成的压力。我们在实地协调框架内提供了适应性抽样算法,证明它是自我稳定和地方最佳的。最后,我们通过模拟来核实,拟议的算法有效地进行了空间适应性抽样,同时保持精确和效率之间的可调和交换。