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.

@article{paperbot2162,
  title = {Space-Fluid Adaptive Sampling by Self-Organisation},
  author = {Roberto Casadei and Stefano Mariani and Danilo Pianini and Mirko Viroli and Franco Zambonelli},
  journal = {Logical Methods in Computer Science},
  volume = {Volume 19, Issue 4},
  year = {2023},
  doi = {10.46298/lmcs-19(4:29)2023}
}