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Interplay between signaling network design and swarm dynamics

Published online by Cambridge University Press:  23 May 2016

ANDRÉ SEKUNDA ,
MOHAMMAD KOMAREJI  and
ROLAND BOUFFANAIS
ANDRÉ SEKUNDA
Affiliation:
Aalborg University, Fredrik Bajers Vej 5, 9220 Aalborg Ø, Denmark (e-mail: [email protected])
MOHAMMAD KOMAREJI
Affiliation:
SUTD–MIT International Design Centre, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore (e-mail: [email protected])
ROLAND BOUFFANAIS
Affiliation:
Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore (e-mail: [email protected])
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Abstract

Distributed information transfer is of paramount importance to the effectiveness of dynamic collective behaviors, especially when a swarm is confronted with complex environmental circumstances. Recently, the signaling network of interaction underlying such effective information transfers has been revealed in the particular case of bird flocks governed by a topological interaction. Such biological systems are known to be evolutionary optimized, but are also constrained by the very nature of the signaling mechanisms—owing to intrinsic limitations in sensory modalities—enabling communication among individuals. Here, we propose that artificial swarm design can be tackled from the angle of signaling network design. To this aim, we use different network models to investigate the impact of some network structural properties on the effectiveness of a specific emergent swarming behavior, namely global consensus. Two new network models are introduced, which together with the well-known Watts–Strogatz model form the basis for an analysis of the relationship between clustering, shortest path and speed to consensus. A network-theoretic approach combined with spectral graph theory tools are used to propose some signaling network design principles. Eventually, one key design principle—a concomitant reduction in clustering and connecting path—is successfully tested on simulations of swarms of self-propelled particles.

Keywords

interaction networkswarming systemstemporal networks
Type
Research Article
Information
Network Science , Volume 4 , Issue 2 , June 2016 , pp. 244 - 265
Copyright
Copyright © Cambridge University Press 2016 

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References

Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008). Synchronization in complex networks. Physics Reports, 469, 93–153.Google Scholar
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., . . . Zdravkovic, V. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences USA, 105, 1232–1237.Google Scholar
Barrat, A., Barthélemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge, U.K.: Cambridge University Press.Google Scholar
Bouffanais, R. (2016). Design and control of swarm dynamics. Heidelberg: Springer.Google Scholar
Bouffanais, R., Weymouth, G. D., & Yue, D. K. P. (2011). Hydrodynamic object recognition using pressure sensing. Proceedings of the Royal Society A, 467, 19–38.Google Scholar
Bouffanais, R., & Yue, D. K. P. (2010). Hydrodynamics of cell-cell mechanical signaling in the initial stages of aggregation. Physical Review E, 81, 041920.Google Scholar
Butail, S., & Paley, D. A. (2012). Three-dimensional reconstruction of the fast-start swimming kinematics of densely schooling fish. Journal of The Royal Society Interface, 9, 77–88.Google Scholar
Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton, NJ: Princeton University Press.Google Scholar
Chung, F. R. K. (1996). Spectral Graph Theory. (CBMS Regional Conference Series in Mathematics, No. 92). Providence, RI: American Mathematical Society.Google Scholar
Coombs, S., & Montgomery, J. C. (1999). The enigmatic lateral line system. In Fay, R. R., & Popper, A. N. (Eds.), Comparative hearing: Fish and amphibians, springer handbook of auditory research (pp. 319–362). New York, NY: Springer-Verlag.Google Scholar
Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision making in animal groups on the move. Nature, 433, 513–516.Google Scholar
Croft, D. P., James, R., & Krause, J. (2008). Exploring animal social networks. Princeton, NJ: Princeton University Press.Google Scholar
Dall, J., & Christensen, M. (2002). Random geometric graphs. Physical Review E, 66, 016121.Google Scholar
Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76, 026107.Google Scholar
Ginelli, F., & Chaté, H. (2010). Relevance of metric-free interactions in flocking phenomena. Physical Review Letters, 105, 168103.Google Scholar
Gross, T., & Blasius, B. (2008). Adaptive coevolutionary networks: A review. Journal of the Royal Society Interface, 5, 259–271.Google Scholar
Hemelrijk, C. K., & Hildenbrandt, H. (2012). Schools of fish and flocks of birds: Their shape and internal structure by self-organization. Interface Focus, 2, 726–737.Google Scholar
Holme, P., & Kim, B. J. (2002). Growing scale-free networks with tunable clustering. Physical Review E, 65, 026107.Google Scholar
Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519, 97–125.CrossRefGoogle Scholar
Hsieh, M. A., Kumar, V., & Chaimowicz, L. (2008). Decentralized controllers for shape generation with robotic swarms. Robotica, 26 (8), 691–701.Google Scholar
Katz, Y., Tunström, K., Ioannou, C. C., Huepe, C., & Couzin, I. D. (2011). Inferring the structure and dynamics of interactions in schooling fish. Proceedings of the National Academy of Sciences USA, 108, 18720–18725.Google Scholar
Komareji, M., & Bouffanais, R. (2013a). Controllability of a swarm of topologically interacting autonomous agents. International Journal of Complex Systems in Science, 3, 11–19.Google Scholar
Komareji, M., & Bouffanais, R. (2013b). Resilience and controllability of dynamic collective behaviors. PLoS one, 8, e82578.Google Scholar
Lemasson, B. H., Anderson, J. J., & Goodwin, R. A. (2013). Motion-guided attention promotes adaptive communications during social navigation. Proceedings of the Royal Society B, 280, 20122003.Google Scholar
Liu, Y.-Y., Slotine, J.-J., & Barabási, A.-L. (2011). Controllability of complex networks. Nature, 473, 167–173.Google Scholar
Liu, Y.-Y., Slotine, J.-J., & Barabási, A.-L. (2012). Control centrality and hierarchical structure in complex networks. PLoS one, 7 (9), e44459.Google Scholar
Moussaïd, M., Helbing, D., & Theraulaz, G. (2011). How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences USA, 108, 6884–6888.Google Scholar
Naruse, K. (2013). Velocity correlation in swarm robots with directional neighborhood. In Lee, S., Cho, H. S., Yoon, K. J., & Lee, J. M. (Eds.), Intelligent autonomous systems 12 (pp. 843–851). Advances in Intelligent Systems and Computing. Berlin: Springer-Verlag.Google Scholar
Olfati-Saber, R. (2005). Ultrafast consensus in small-world networks. Proc. Am. Control Conf., pp. 2371–2378.Google Scholar
Olfati-Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on Automatic Control, 51, 401–420.Google Scholar
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95 (1), 215–233.Google Scholar
Shang, Y., & Bouffanais, R. (2014a). Consensus reaching in swarms ruled by a hybrid metric-topological distance. European Physical Journal B, 87, 294.Google Scholar
Shang, Y., & Bouffanais, R. (2014b). Influence of the number of topologically interacting neighbors on swarm dynamics. Scientific Reports, 4, 4184.Google Scholar
Strandburg-Peshkin, A., Twomey, C. R., Bode, N. W., Kao, A. B., Katz, Y., Ioannou, C. C., ref. . . Couzin, I. D. (2013). Visual sensory networks and effective information transfer in animal groups. Current Biology, 23 (17), R709–R711.Google Scholar
Sumpter, D., Buhl, J., Biro, D., & Couzin, I. (2008). Information transfer in moving animal groups. Theory Bioscience, 127, 177–186.Google Scholar
Sumpter, D. J. T. (2006). The principles of collective animal behaviour. Philosophical Transactions of the Royal Society B, 361, 5–22.Google Scholar
Sumpter, D. J. T. (2010). Collective animal behavior. Princeton, NJ: Princeton University Press.Google Scholar
Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase-transition in a system of self-driven particles. Physical Review Letters, 75, 1226–1229.Google Scholar
Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517, 71–140.Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.Google Scholar
Xu, W., & Liu, Z. (2008). How community structure influences epidemic spread in social networks. Physica A, 387, 623–630.Google Scholar
Young, G. F., Scardovi, L., Cavagna, A., Giardina, I., & Leonard, N. E. (2013). Starling flock networks manage uncertainty in consensus at low cost. PLoS Computational Biology, 9 (1), e1002894.Google Scholar