2,552 research outputs found
Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation
We propose a multi-hop diffusion strategy for a sensor network to perform
distributed least mean-squares (LMS) estimation under local and network-wide
energy constraints. At each iteration of the strategy, each node can combine
intermediate parameter estimates from nodes other than its physical neighbors
via a multi-hop relay path. We propose a rule to select combination weights for
the multi-hop neighbors, which can balance between the transient and the
steady-state network mean-square deviations (MSDs). We study two classes of
networks: simple networks with a unique transmission path from one node to
another, and arbitrary networks utilizing diffusion consultations over at most
two hops. We propose a method to optimize each node's information neighborhood
subject to local energy budgets and a network-wide energy budget for each
diffusion iteration. This optimization requires the network topology, and the
noise and data variance profiles of each node, and is performed offline before
the diffusion process. In addition, we develop a fully distributed and adaptive
algorithm that approximately optimizes the information neighborhood of each
node with only local energy budget constraints in the case where diffusion
consultations are performed over at most a predefined number of hops. Numerical
results suggest that our proposed multi-hop diffusion strategy achieves the
same steady-state MSD as the existing one-hop adapt-then-combine diffusion
algorithm but with a lower energy budget.Comment: 14 pages, 12 figures. Submitted for publicatio
Greedy Gossip with Eavesdropping
This paper presents greedy gossip with eavesdropping (GGE), a novel
randomized gossip algorithm for distributed computation of the average
consensus problem. In gossip algorithms, nodes in the network randomly
communicate with their neighbors and exchange information iteratively. The
algorithms are simple and decentralized, making them attractive for wireless
network applications. In general, gossip algorithms are robust to unreliable
wireless conditions and time varying network topologies. In this paper we
introduce GGE and demonstrate that greedy updates lead to rapid convergence. We
do not require nodes to have any location information. Instead, greedy updates
are made possible by exploiting the broadcast nature of wireless
communications. During the operation of GGE, when a node decides to gossip,
instead of choosing one of its neighbors at random, it makes a greedy
selection, choosing the node which has the value most different from its own.
In order to make this selection, nodes need to know their neighbors' values.
Therefore, we assume that all transmissions are wireless broadcasts and nodes
keep track of their neighbors' values by eavesdropping on their communications.
We show that the convergence of GGE is guaranteed for connected network
topologies. We also study the rates of convergence and illustrate, through
theoretical bounds and numerical simulations, that GGE consistently outperforms
randomized gossip and performs comparably to geographic gossip on
moderate-sized random geometric graph topologies.Comment: 25 pages, 7 figure
INDIGO: An In Situ Distributed Gossip Framework for Sensor Networks
Abstract—With the onset of Cyber-Physical Systems (CPS), distributed algorithms on Wireless Sensor Networks(WSNs) have been receiving renewed attention. The distributed consensus problem is a well studied problem having a myriad of applications which can be accomplished using asynchronous distributed gossip algorithms on Wireless Sensor Networks(WSN). However, a practical realization of gossip algorithms for WSNs is found lacking in the current state of the art. In this paper, we propose the design, development and analysis of a novel in-situ distributed gossip framework called INDIGO. A key aspect of INDIGO is its ability to execute on a generic system platform as well as on a hardware oriented testbed platform in a seamless manner allowing easy portability of existing algorithms. We evaluate the performance of INDIGO with respect to the distributed consensus problem as well as the distributed optimization problem. We also present a data driven analysis of the effect, certain operating parameters like sleep time and wait time have on the performance of the framework and empirically attempt to determine a sweet spot. The results obtained from various experiments on INDIGO validate its efficacy, reliability and robustness and demonstrate its utility as a framework for the evaluation and implementation of asynchronous distributed algorithms
FROST -- Fast row-stochastic optimization with uncoordinated step-sizes
In this paper, we discuss distributed optimization over directed graphs,
where doubly-stochastic weights cannot be constructed. Most of the existing
algorithms overcome this issue by applying push-sum consensus, which utilizes
column-stochastic weights. The formulation of column-stochastic weights
requires each agent to know (at least) its out-degree, which may be impractical
in e.g., broadcast-based communication protocols. In contrast, we describe
FROST (Fast Row-stochastic-Optimization with uncoordinated STep-sizes), an
optimization algorithm applicable to directed graphs that does not require the
knowledge of out-degrees; the implementation of which is straightforward as
each agent locally assigns weights to the incoming information and locally
chooses a suitable step-size. We show that FROST converges linearly to the
optimal solution for smooth and strongly-convex functions given that the
largest step-size is positive and sufficiently small.Comment: Submitted for journal publication, currently under revie
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