{"id":1032,"date":"2024-04-01T11:35:19","date_gmt":"2024-04-01T18:35:19","guid":{"rendered":"https:\/\/labs.engineering.asu.edu\/sops\/?page_id=1032"},"modified":"2024-04-01T11:35:19","modified_gmt":"2024-04-01T18:35:19","slug":"energy-distribution","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/sops\/energy-distribution\/","title":{"rendered":"Bio-Inspired Energy Distribution for Programmable Matter"},"content":{"rendered":"\n
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Joshua J. Daymude, Andr\u00e9a W. Richa, and Jamison W. Weber.

[arXiv<\/a>] [Online<\/a>] \u2013 Conference paper at the\u00a022nd\u00a0International Conference on Distributed Computing and Networking (ICDCN \u201921)<\/em>,\u00a0pp. 86\u201395, 2021.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Abstract<\/h2>\n\n\n\n

In systems of active programmable matter<\/em>, individual modules require a constant supply of energy to participate in the system\u2019s collective behavior. These systems are often powered by an external energy source<\/em> accessible by at least one module and rely on module-to-module power transfer<\/em> 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<\/em> in the amoebot model<\/em> that is loosely inspired by the growth behavior of Bacillus subtilis<\/em> bacterial biofilms<\/a>. These bacteria use chemical signalling 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 harvest sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model\u2019s well-established spanning forest primitive<\/em> 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.<\/p>\n\n\n\n

Overview<\/h2>\n\n\n\n

In short, individual bacterium that are metabolically stressed (lacking in nutrients) catalyze a wave of chemical signaling that inhibits the rest of the biofilm\u2019s nutrient uptake. This allows more nutrients to flow to the stressed bacteria, effectively solving the energy distribution problem. We model energy constraints in the amoebot model by giving each particle a battery for storing energy that it can transfer to its neighbors or spend to perform actions. We assume at least one particle in the system has access to an external energy source<\/em> from which it can harvest energy. The goal of our energy distribution algorithm is to coordinate the transfer of energy between particles so that all particles meet their actions\u2019 energy demands and at least one particle can actually perform its action within a bounded amount of time. After some initial setup, our Energy-Sharing<\/strong> algorithm works in the following phases repeated by each particle individually:<\/p>\n\n\n\n