TY - GEN
T1 - Bio-Inspired Energy Distribution for Programmable Matter
AU - Daymude, Joshua J.
AU - Richa, Andréa W.
AU - Weber, Jamison W.
N1 - Funding Information:
The authors gratefully acknowledge their support from the National Science Foundation under awards CCF-1637393 and CCF-1733680 and from the U.S. Department of Defense under MURI award #W911NF-19-1-0233. We thank Prof. Deborah Gordon and Prof. Saket Navlakha for their pointers to research on bacterial biofilms and their helpful discussions that initiated this work. We also thank Prof. Theodore Pavlic for generously sharing his knowledge of bio-inspired approaches to energy management in swarm robotics. Finally, we thank undergraduate researcher Christopher Boor for his contributions to a preliminary version of this work.
Publisher Copyright:
© 2021 ACM.
PY - 2021/1/5
Y1 - 2021/1/5
N2 - In systems of active programmable matter, individual modules require a constant supply of energy to participate in the system's collective behavior. These systems are often powered by an external energy source accessible by at least one module and rely on module-to-module power transfer to distribute energy throughout the system. While much effort has gone into addressing challenging aspects of power management in programmable matter hardware, algorithmic theory for programmable matter has largely ignored the impact of energy usage and distribution on algorithm feasibility and efficiency. In this work, we present an algorithm for energy distribution in the amoebot model that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. These bacteria use chemical signaling to communicate their metabolic states and regulate nutrient consumption throughout the biofilm, ensuring that all bacteria receive the nutrients they need. Our algorithm similarly uses communication to inhibit energy usage when there are starving modules, enabling all modules to receive sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model's well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.
AB - In systems of active programmable matter, individual modules require a constant supply of energy to participate in the system's collective behavior. These systems are often powered by an external energy source accessible by at least one module and rely on module-to-module power transfer to distribute energy throughout the system. While much effort has gone into addressing challenging aspects of power management in programmable matter hardware, algorithmic theory for programmable matter has largely ignored the impact of energy usage and distribution on algorithm feasibility and efficiency. In this work, we present an algorithm for energy distribution in the amoebot model that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. These bacteria use chemical signaling to communicate their metabolic states and regulate nutrient consumption throughout the biofilm, ensuring that all bacteria receive the nutrients they need. Our algorithm similarly uses communication to inhibit energy usage when there are starving modules, enabling all modules to receive sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model's well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.
KW - biofilms
KW - biologically-inspired algorithms
KW - distributed algorithms
KW - energy
KW - programmable matter
KW - self-organization
UR - http://www.scopus.com/inward/record.url?scp=85098735232&partnerID=8YFLogxK
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U2 - 10.1145/3427796.3427835
DO - 10.1145/3427796.3427835
M3 - Conference contribution
AN - SCOPUS:85098735232
T3 - ACM International Conference Proceeding Series
SP - 86
EP - 95
BT - ICDCN 2021 - Proceedings of the 2021 International Conference on Distributed Computing and Networking
PB - Association for Computing Machinery
T2 - 22nd International Conference on Distributed Computing and Networking, ICDCN 2021
Y2 - 5 January 2021 through 8 January 2021
ER -