Sensing enhancement on complex networks
Sensing enhancement on complex networks
Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f
Brede, Markus and Romero Moreno, Guillermo
(2021)
Sensing enhancement on complex networks.
In Proceedings of the Conference on Complex Networks and their Applications 2021.
Springer..
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Previous work has shown that communication between agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that whilst bestowing advantages on a small group of agents degree heterogeneity tends to impede overall sensing enhancement, while clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that for low connectivity sensing enhancement is maximised by random regular networks, whereas for large connectivity best sensing enhancement is found for ring graphs.
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Accepted/In Press date: 2021
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Local EPrints ID: 452046
URI: http://eprints.soton.ac.uk/id/eprint/452046
PURE UUID: 259ef83d-0b50-4e7e-bd94-6411c19c16f9
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Date deposited: 09 Nov 2021 17:34
Last modified: 23 Feb 2023 03:17
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Contributors
Author:
Markus Brede
Author:
Guillermo Romero Moreno
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