Results 11  20
of
53
On Corrective Consensus: Converging to the Exact Average
"... Consensus algorithms provide an elegant, distributed way for computing the average of a set of measurements across a sensor network. However, the convergence of the node estimates to the global average depends on the timely and reliable exchange of the measurements to neighboring sensors. These assu ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
(Show Context)
Consensus algorithms provide an elegant, distributed way for computing the average of a set of measurements across a sensor network. However, the convergence of the node estimates to the global average depends on the timely and reliable exchange of the measurements to neighboring sensors. These assumptions are violated in practice due to random packet losses, causing the estimated average to be biased. In this paper, we present and analyze a practical consensus protocol that overcomes these difficulties and assures convergence to the correct average. Simulation results show that the proposed corrective consensus requires ten times less overhead to reach the same level of accuracy as the one achieved by a variant of standard consensus that uses retransmissions to (partially) overcome the negative effects of packet losses. In networks with more severe packet loss rates, corrective consensus is more than forty times more accurate than standard consensus that uses retransmissions. More importantly, by continuing to execute the corrective consensus algorithm the estimation error can become arbitrarily small. 1
Low Complexity Resilient Consensus in Networked MultiAgent Systems with Adversaries
"... Recently, many applications have arisen in distributed control that require consensus protocols. Concurrently, we have seen a proliferation of malicious attacks on largescale distributed systems. Hence, there is a need for (i) consensus problems that take into consideration the presence of adversar ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
Recently, many applications have arisen in distributed control that require consensus protocols. Concurrently, we have seen a proliferation of malicious attacks on largescale distributed systems. Hence, there is a need for (i) consensus problems that take into consideration the presence of adversaries and specify correct behavior through appropriate conditions on agreement and safety, and (ii) algorithms for distributed control applications that solve such consensus problems resiliently despite breaches in security. This paper addresses these issues by (i) defining the adversarial asymptotic agreement problem, which requires that the uncompromised agents asymptotically align their states while satisfying an invariant condition in the presence of adversaries, and (ii) by designing a low complexity consensus protocol, the Adversarial Robust Consensus Protocol (ARCP), which combines ideas from distributed computing and cooperative control. Two types of omniscient adversaries are considered: (i) Byzantine agents can convey different state trajectories to different neighbors in the network, and (ii) malicious agents must convey the same information to each neighbor. For each type of adversary, sufficient conditions are provided that ensure ARCP guarantees the agreement and safety conditions in static and switching network topologies, whenever the number of adversaries in the network is bounded by a constant. The conservativeness of the conditions is examined, and the conditions are compared to results in the literature.
Unreliable and ResourceConstrained Decoding
, 2010
"... Traditional information theory and communication theory assume that decoders are noiseless and operate without transient or permanent faults. Decoders are also traditionally assumed to be unconstrained in physical resources like materiel, memory, and energy. This thesis studies how constraining reli ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
Traditional information theory and communication theory assume that decoders are noiseless and operate without transient or permanent faults. Decoders are also traditionally assumed to be unconstrained in physical resources like materiel, memory, and energy. This thesis studies how constraining reliability and resources in the decoder limits the performance of communication systems. Five communication problems are investigated. Broadly speaking these are communication using decoders that are wiring costlimited, that are memorylimited, that are noisy, that fail catastrophically,
Routing for statistical inference in sensor networks
 IN HANDBOOK ON ARRAY PROCESSING AND SENSOR NETWORKS, S. HAYKIN AND
, 2008
"... In the classical approach, the problem of distributed statistical inference and the problem of minimum cost routing of the measurements to the fusion center are treated separately. Such schemes cannot exploit the “inherent” saving in routing costs arising from data reduction in a sufficient statisti ..."
Abstract

Cited by 5 (5 self)
 Add to MetaCart
In the classical approach, the problem of distributed statistical inference and the problem of minimum cost routing of the measurements to the fusion center are treated separately. Such schemes cannot exploit the “inherent” saving in routing costs arising from data reduction in a sufficient statistic for inference. Our approach is to conduct innetwork processing of the likelihood function which is the minimal sufficient statistic and deliver it to the fusion center for inference. We employ the Markov random field (MRF) model for spatial correlation of sensor data. The structure of the likelihood function is well known for a MRF from the famous HammersleyClifford theorem. Exploiting this structure, we show that the minimum cost routing for computation and delivery of the likelihood function is a Steiner tree on a transformed graph. This Steinertree reduction preserves the approximation ratio, which implies that any Steinertree approximation can be employed for minimum cost fusion with the same approximation ratio. In this chapter, we present an overview of this approach to minimum cost fusion.
Nonparametric inference and coordination for distributed robotics
 In Proceedings of the IEEE Conference on Decision and Control
, 2012
"... Abstract — This paper presents nonparametric methods to infer the state of an environment by distributively controlling robots equipped with sensors. Each robot represents its belief of the environment state with a weighted sample set, which is used to draw likely observations to approximate the gr ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Abstract — This paper presents nonparametric methods to infer the state of an environment by distributively controlling robots equipped with sensors. Each robot represents its belief of the environment state with a weighted sample set, which is used to draw likely observations to approximate the gradient of mutual information. The gradient leads to a novel distributed controller that continuously moves the robots to maximize the informativeness of the next joint observation, which is then used to update the weighted sample set via a sequential Bayesian filter. The incorporated nonparametric methods are able to robustly represent the environment state and robots’ observations even when they are modeled as continuousvalued random variables having complicated multimodal distributions. In addition, a consensusbased algorithm allows for the distributed approximation of the joint measurement probabilities, where these approximations provably converge to the true probabilities even when the number of robots, the maximum in/out degree, and the network diameter are unknown. The approach is implemented for five quadrotor flying robots deployed over a large outdoor environment, and the results of two separate exploration tasks are discussed. I.
Social Learning and Distributed Hypothesis Testing
"... ISIT Student Paper Award). This paper considers the problem of distributed hypothesis testing and social learning. Suppose individual nodes in a network receive noisy (private) observations whose distribution is parameterized by one of M parameters (hypotheses). The distributions are known locally ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
ISIT Student Paper Award). This paper considers the problem of distributed hypothesis testing and social learning. Suppose individual nodes in a network receive noisy (private) observations whose distribution is parameterized by one of M parameters (hypotheses). The distributions are known locally at the nodes, but the true parameter/hypothesis is not known. If the local observations are insufficient to recover the underlying parameter (for example, low dimensional measurements of a higherdimensional parameter), individuals must share and learn from each other in order to accurately infer the true parameter. Inspired by recent nonBayesian social learning algorithms, the updating of opinions of each node is broken down into two steps: a localBayesian update, which incorporates the noisy observations and the known parametric distribution of the noise, and a new nonBayesian update rule which merges the nodes ’ opinions. It is shown that each node’s opinion/belief about any hypothesis whose truth is inconsistent with the overall networkwide observations vanishes to zero exponentially fast. In other words, each node’s opinion converges to the true underlying parameter exponentially fast. This new method of merging opinions allows for a concise proof of the convergence and a closed form characterization of rate of convergence. Furthermore, the exponential rate of learning is shown to be both a function of the nodes ’ collective ability to discriminate among the hypotheses set as well as the social structure of the network. I.
Broadcast Gossip Based Distributed Hypothesis Testing in Wireless Sensor Networks
"... Abstract—We consider the scenario that N sensors collaborate to observe a single event. The sensors are distributed and can only exchange messages through a network to reach a consensus about the observed event. In this paper, we propose a very robust and simple method using broadcast gossip algorit ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract—We consider the scenario that N sensors collaborate to observe a single event. The sensors are distributed and can only exchange messages through a network to reach a consensus about the observed event. In this paper, we propose a very robust and simple method using broadcast gossip algorithm to solve the distributed hypothesis testing problem. The simulation result shows that our method has good performance and is very energy efficient comparing to existing methods. I.
Team Optimal Control of Coupled Subsystems with MeanField Sharing
"... Abstract — We investigate team optimal control of stochastic subsystems that are weakly coupled in dynamics (through the meanfield of the system) and are arbitrary coupled in the cost. The controller of each subsystem observes its local state and the meanfield of the state of all subsystems. The s ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
Abstract — We investigate team optimal control of stochastic subsystems that are weakly coupled in dynamics (through the meanfield of the system) and are arbitrary coupled in the cost. The controller of each subsystem observes its local state and the meanfield of the state of all subsystems. The system has a nonclassical information structure. Exploiting the symmetry of the problem, we identify an information state and use that to obtain a dynamic programming decomposition. This dynamic program determines a globally optimal strategy for all controllers. Our solution approach works for arbitrary number of controllers and generalizes to the setup when the meanfield is observed with noise. The size of the information state is timeinvariant; thus, the results generalize to the infinitehorizon control setups as well. In addition, when the meanfield is observed without noise, the size of the corresponding information state increases polynomially (rather than exponentially) with the number of controllers which allows us to solve problems with moderate number of controllers. We illustrate our approach by an example motivated by smart grids that consists of 100 coupled subsystems.
Increasingly correct message passing algorithms for heat source detection in sensor networks
 In IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON
, 2004
"... Abstract — We study averaging algorithms, when implemented in large networks of wirelessly connected elements. We extend the notion of “Increasing Correctness ” (IC) which was defined for cyclefree graphs, to general graphs. An averaging algorithm that is IC has meaningful outputs at each iteration ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Abstract — We study averaging algorithms, when implemented in large networks of wirelessly connected elements. We extend the notion of “Increasing Correctness ” (IC) which was defined for cyclefree graphs, to general graphs. An averaging algorithm that is IC has meaningful outputs at each iteration. This makes it possible to stop the algorithm at any time, and use the output values computed up to that time. We prove that the class of IC averaging algorithms is nontrivial. We then present a simple IC averaging algorithm that is based on ideas from Graphical Models, and study its properties. Finally, we give example applications and simulations of IC averaging algorithms. I.