一句话：提出基于场协调的自组织自适应采样算法，通过动态竞争区域划分空间，并证明其自稳定与局部最优。
协调系统常需处理随空间变化的信号，如分布式感知数据或计算结果；大规模场景可用去中心化、情境化系统，节点本地感知、处理并与邻居协调。本文在场协调框架下设计分布式策略，用协作式自适应采样估计空间现象：空间动态划分为可竞争、可生长/收缩的区域，形成随底层现象压力调整的“流体”虚拟空间。作者提出算法，证明其具有自稳定性与局部最优性，并通过仿真验证该算法能有效完成空间自适应采样，且在精度与效率之间保持可调权衡。
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}
}