Stacy Patterson

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We consider distributed consensus and vehicular formation control problems. Specifically we address the question of whether local feedback is sufficient to maintain coherence in large-scale networks subject to stochastic disturbances. We define macroscopic performance measures which are global quantities that capture the notion of coherence; a notion of(More)
We consider the problem of leader-based distributed coordination in networks where agents are subject to stochastic disturbances, but where certain designated leaders are immune to those disturbances. Specifically, we address the effect of leader selection on the coherence of the network, defined in terms of an H2 norm of the system. This quantity captures(More)
A noteworthy advance in distributed computing is due to the recent development of peer-to-peer systems. These systems are essentially dynamic in the sense that no process can get a global knowledge on the system structure. They mainly allow processes to look up for data that can be dynamically added/suppressed in a permanently evolving set of nodes.(More)
We consider a distributed average consensus algorithm over a network in which communication links fail with independent probability. In such stochastic networks, convergence is defined in terms of the variance of deviation from average. We first show how the problem can be recast as a linear system with multiplicative random inputs which model link(More)
We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements using only communication with neighbors in the network. Our distributed approach to this problem is based on the centralized Iterative(More)
We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements at a minimal communication cost and with low computational complexity. A naïve(More)
We consider a distributed average consensus algorithm over a network in which communication links fail with independent probability. Convergence in such stochastic networks is defined in terms of the variance of deviation from average. We characterize the decay factor of the variance in terms of the eigenvalues of a Lyapunov-like matrix recursion. We give(More)
We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google’s Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction management and(More)
We study distributed consensus algorithms in fractal networks where agents are subject to external disturbances. We characterize the coherence of these networks in terms of an H2 norm of the system that captures how closely agents track the consensus value. We show that, in first-order systems, the coherence measure is closely related to the global mean(More)