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BY 4.0 license Open Access Published by De Gruyter Open Access March 1, 2019

Focusing attention in populations of semi-autonomously operating sensing nodes

  • Hanno Hildmann EMAIL logo , Miguel Almeida , Abdel F. Isakovic and Fabrice Saffre


Cognition and the cognitive processing of sensory information in biological entities is known to occur over multiple layers of processing. In the example of human vision there are a vast number of photo-receptors feeding into various layers of cells which pre-process the original information before it arrives to the brain (as biased data).We propose to use a mechanism known to theoretical biologists as a means to bring about adaptive selforganization in colonies of social insects, and to apply it to such early stage signal processing. The underlying mathematical model is simple, and in the coming years, robotics will move into an era when aggregating simple computation devices into massively large collectives becomes feasible, making it possible to actually build such distributed cognitive sensing systems.


[1] A. Bandura, Social cognitive theory: an agentic perspective, Annual Review of Psychology, 2001, 52(1), 1-2610.1146/annurev.psych.52.1.1Search in Google Scholar PubMed

[2] R. Desimone, J. Duncan, Neural mechanisms of selective visual attention, Annual Review of Neuroscience, 1995, 18, 193-22210.1146/ in Google Scholar PubMed

[3] P. R. Roelfsema, Cortical algorithms for perceptual grouping, Annual Review of Neuroscience, 2006, 29(1), 203-22710.1146/annurev.neuro.29.051605.112939Search in Google Scholar PubMed

[4] J. R. Anderson, Cognitive Psychology and Its Implications, A series of books in psychology, W. H. Freeman, 1995Search in Google Scholar

[5] K. McAlonan, J. Cavanaugh, R. H. Wurtz, Guarding the gateway to cortex with attention in visual thalamus, Nature, 2008, 456(7220), 391-39410.1038/nature07382Search in Google Scholar PubMed PubMed Central

[6] P. R. Roelfsema, Attention - voluntary control of brain cells, Science, 2011, 332(6037), 1512-151310.1126/science.1208564Search in Google Scholar PubMed

[7] J. P. Gottlieb, M. Kusunoki, M. E. Goldberg, The representation of visual salience in monkey parietal cortex, Nature, 1998, 391(6666), 481-48410.1038/35135Search in Google Scholar PubMed

[8] J.-L. Deneubourg, Application de l’ordre par fluctuations a la description de certaines étapes de la construction du nid chez les termites, Insectes Sociaux, 1977, 24(2), 117-13010.1007/BF02227166Search in Google Scholar

[9] Patent Application, Method and system for providing data from a plurality of sensing devices, PCT/EP2015/062810 (pending), H. Hildmann, S. Nicolas, M. Martin (inventors), Dec. 2016Search in Google Scholar

[10] M. Almeida, H. Hildmann, G. Solmaz, Distributed UAVswarmbased real-time geomatic data collection under dynamically changing resolution requirements, In: UAV-g 2017 - International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, Sept. 201710.5194/isprs-archives-XLII-2-W6-5-2017Search in Google Scholar

[11] S. Camazine, J.-L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz, E. Bonabeau, Self-Organization in Biological Systems, Princeton University Press, 2001Search in Google Scholar

[12] H. Hildmann, S. Nicolas, F. Saffre, A bio-inspired resourcesaving approach to dynamic client-server association, IEEE Intelligent Systems, 2012, 27(6), 17-2510.1109/MIS.2012.84Search in Google Scholar

[13] J. Bartholdi, D. Eisenstein, A production line that balances itself, Operations Research, 1996, 44(1), 21-3410.1287/opre.44.1.21Search in Google Scholar

[14] R. Pfeifer, M. Lungarella, F. Iida, Self-organization, embodiment, and biologically inspired robotics, Science, 2007, 318(5853), 1088-109310.1126/science.1145803Search in Google Scholar PubMed

[15] T. D. Seeley, Honeybee Democracy, Princeton University Press, 201010.1515/9781400835959Search in Google Scholar

[16] R. Beckers, O. Holland, J.-L. Deneubourg, From local actions to global tasks: stigmergy and collective Robots, In: Proceedings of the Workshop on Artificial Life, MIT Press, 1994, 181-189Search in Google Scholar

[17] J.-L. Deneubourg, S. Aron, S. Goss, J. M. Pasteels, The selforganizing exploratory pattern of the argentine ant, Journal of Insect Behavior, 1990, 3(2), 159-16810.1007/BF01417909Search in Google Scholar

[18] E. Bonabeau, M. Dorigo, G. Theraulaz, Inspiration for optimization from social insect behaviour, Nature, 2000, 406, 39-4210.1038/35017500Search in Google Scholar PubMed

[19] H. Hildmann, M. Martin, Adaptive scheduling in dynamic environments, In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Annals of Computer Science and Information Systems, 2014, 2, 1331-133610.15439/2014F357Search in Google Scholar

[20] R. Altawy, A. M. Youssef, Security, privacy, and safety aspects civilian drones: A survey, ACM Transactions on Cyber-Physical Systems, 2016, 1(2), Article No. 710.1145/3001836Search in Google Scholar

[21] F. Saffre, H. Hildmann, J.-L. Deneubourg, Can individual heterogeneity influence self-organised patterns in the termite nest construction model?, Swarm Intelligence, 2018, 12(2), 101-11010.1007/s11721-017-0143-8Search in Google Scholar

[22] M. Cerf, N. Thiruvengadam, F. Mormann, A. Kraskov, R. Q. Quiroga, C. Koch, I. Fried, On-line, voluntary control of human temporal lobe neurons, Nature, 2010, 467(7319), 1104-110810.1038/nature09510Search in Google Scholar PubMed PubMed Central

[23] E. P. Simoncelli, B. A. Olshausen, Natural image statistics and neural representation, Annual Review of Neuroscience, 2001, 24, 1193-121610.1146/annurev.neuro.24.1.1193Search in Google Scholar PubMed

Received: 2018-11-14
Accepted: 2019-01-15
Published Online: 2019-03-01

© 2019 Hanno Hildmann, et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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