3 research outputs found
Distributed anonymous discrete function computation
We propose a model for deterministic distributed function computation by a
network of identical and anonymous nodes. In this model, each node has bounded
computation and storage capabilities that do not grow with the network size.
Furthermore, each node only knows its neighbors, not the entire graph. Our goal
is to characterize the class of functions that can be computed within this
model. In our main result, we provide a necessary condition for computability
which we show to be nearly sufficient, in the sense that every function that
satisfies this condition can at least be approximated. The problem of computing
suitably rounded averages in a distributed manner plays a central role in our
development; we provide an algorithm that solves it in time that grows
quadratically with the size of the network
Efficient Information Aggregation Strategies for Distributed Control and Signal Processing
This thesis is concerned with distributed control and coordination of
networks consisting of multiple, potentially mobile, agents. This is motivated
mainly by the emergence of large scale networks characterized by the lack of
centralized access to information and time-varying connectivity. Control and
optimization algorithms deployed in such networks should be completely
distributed, relying only on local observations and information, and robust
against unexpected changes in topology such as link failures. We will describe
protocols to solve certain control and signal processing problems in this
setting. We will demonstrate that a key challenge for such systems is the
problem of computing averages in a decentralized way. Namely, we will show that
a number of distributed control and signal processing problems can be solved
straightforwardly if solutions to the averaging problem are available. The rest
of the thesis will be concerned with algorithms for the averaging problem and
its generalizations. We will (i) derive the fastest known averaging algorithms
in a variety of settings and subject to a variety of communication and storage
constraints (ii) prove a lower bound identifying a fundamental barrier for
averaging algorithms (iii) propose a new model for distributed function
computation which reflects the constraints facing many large-scale networks,
and nearly characterize the general class of functions which can be computed in
this model.Comment: Ph.D. thesis, Department of Electrical Engineering and Computer
Science, MIT, September 201