# Space-Fluid Adaptive Sampling by Self-Organisation

arXiv: https://arxiv.org/abs/2210.17505 · HTML: https://mlsanity.com/paper/2210.17505/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 71 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [A Higher-order Calculus of Computational Fields](https://mlsanity.com/paper/1610.08116/) (2016)
- [Engineering Resilient Collective Adaptive Systems by Self-Stabilisation](https://mlsanity.com/paper/1711.08297/) (2017)
- [Time-Fluid Field-Based Coordination through Programmable Distributed Schedulers](https://mlsanity.com/paper/2012.13806/) (2020)
- [Field-based Coordination with the Share Operator](https://mlsanity.com/paper/1910.02874/) (2019)
- [Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots](https://mlsanity.com/paper/1804.10544/) (2018)
- [Space-Time Sampling for Network Observability](https://mlsanity.com/paper/1811.01303/) (2018)
- [Time-optimal self-stabilizing leader election in population protocols](https://mlsanity.com/paper/1907.06068/) (2019)

## Built on by (most cited first, 2 of 2)

- [Proximity-based Self-Federated Learning](https://mlsanity.com/paper/2407.12410/) (2024)
- [FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2502.08577/) (2025)

## Abstract (arXiv)

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.