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This paper proposes three novel nonlinear continuous-time distributed algorithms to solve a class of fair resource allocation problems that allow an interconnected group of agents to collectively minimize a global cost function subject to equality and inequality constraints. The algorithms are robust in the sense that temporary errors in communication or computation do not change their convergence...
Multi-camera visual sensor networks (VSNs) require large computational resources in order to perform visual analysis in real-time. One way to match the computational needs is to augment the VSN with dedicated processing nodes that do in-network processing, but this requires careful allocation of loads from the sensor nodes in order to ensure low processing times. In this paper we formulate the problem...
We present a distributed and decentralized algorithm for graph signal inpainting. The previous work obtained a closed-form solution with matrix inversion. In this paper, we ease the computation by using a distributed algorithm, which solves graph signal inpainting by restricting each node to communicate only with its local nodes. We show that the solution of the distributed algorithm converges to...
The problem of finding network codes for general connections is inherently difficult. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on very restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general...
The ability to recover a low-rank matrix from a subset of its entries is the leitmotif of recent advances for localization of wireless sensors, unveiling traffic anomalies in backbone networks, and preference modeling for recommender systems. This paper develops a distributed algorithm for low-rank matrix completion over networks. While nuclear-norm minimization has well-documented merits when centralized...
This paper deals with an output consensus problem of multiple agents and first presents a centralized algorithm for solving it by a model predictive control method based on linear matrix inequalities. It can be shown that the outputs of all the agents controlled by the presented method asymptotically converge to a common point, i.e., consensus point. Then two kinds of algorithms for solving the consensus...
We consider a wireless sensor network with n sensor nodes. The sensed data needs to be transferred in a multi-hop fashion to a common processing center. We consider the standard data sampling/sensing scheme where the sensor nodes have a sampling process independent of the transmission scheme. In this paper, we study the problem of optimizing the end-to-end delay in a multi-hop single-sink wireless...
In this paper, joint dynamic frequency selection and transmit power control problem with the goal of power minimization for a target SINR is investigated for cognitive radio networks. The optimization problem is formulated and three algorithms are proposed: A centralized algorithm based on graph coloring, and two distributed algorithms based on game theory; one for coordinated and the other for non-coordinated...
Stability and delay constraints have significant impact on the design and operation of wireless sensor networks. In this paper, we propose a closed architecture for data sampling in wireless sensor networks. Examples show that the proposed scheme outperforms the traditional layered scheme, both in terms of stable operating region as well as the end-to-end delays. We then propose a distributed routing...
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