Distributed Adaptive Constrained Optimization for Smart Matter Systems
Markus P.J. Fromherz, Lara S. Crawford, Christophe Guettier, and Yi Shang
Abstract
The remarkable increase in computing power together with a similar
increase in sensor and actuator capabilities now under way is enabling
a significant change in how systems can sense and manipulate their
environment. These changes require control algorithms capable of
operating a multitude of interconnected components. In particular,
novel ``smart matter'' systems will eventually use thousands of
embedded, micro-size sensors, actuators and processors.
In this paper, we propose a new framework for a on-line, adaptive
constrained optimization for distributed embedded applications. In
this approach, on-line optimization problems are decomposed and
distributed across the network, and solvers are controlled by an
adaptive feedback mechanism that guarantees timely solutions.
We also present examples from our experience in implementing smart
matter systems to motivate our ideas.
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