Space-Fluid Adaptive Sampling by Self-Organisation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866910327695736832 |
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| author | Casadei, Roberto Mariani, Stefano Pianini, Danilo Viroli, Mirko Zambonelli, Franco |
| author_facet | Casadei, Roberto Mariani, Stefano Pianini, Danilo Viroli, Mirko Zambonelli, Franco |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2210_17505 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Space-Fluid Adaptive Sampling by Self-Organisation Casadei, Roberto Mariani, Stefano Pianini, Danilo Viroli, Mirko Zambonelli, Franco Distributed, Parallel, and Cluster Computing Artificial Intelligence Multiagent Systems Systems and Control I.2.11; D.3.1; D.1.3 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. |
| title | Space-Fluid Adaptive Sampling by Self-Organisation |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Multiagent Systems Systems and Control I.2.11; D.3.1; D.1.3 |
| url | https://arxiv.org/abs/2210.17505 |