27,047 research outputs found
Global Consensus through Local Synchronization (Technical Report)
Coordination languages have emerged for the specification and implementation of interaction protocols among concurrent entities. Currently, we are developing a code generator for one such a language, based on the formalism of constraint automata (CA). As part of the compilation process, our tool computes the CA-specific synchronous product of a number of CA, each of which models a constituent of the protocol to generate code for. This ensures that implementations of those CA at run-time reach a consensus about their global behavior in every step. However, using the existing product operator on CA can be practically problematic. In this paper, we provide a solution by defining a new, local product operator on CA that avoids those problems. We then identify a sufficiently large class of CA for which using our new product instead of the existing one is semantics-preserving. Finally, we describe how to apply this result to code generation and also sketch how to use the same theory for projecting choreographies
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Nonreciprocal Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights dependent on the radio interface and we pose special
attention to the effect of the propagation delays occurring in the exchange of
data among sensors, as a function of the network geometry. We derive necessary
and sufficient conditions for the proposed system to reach a consensus on
globally optimal decision statistics. One of the major results proved in this
work is that a consensus is achieved for any bounded delay condition if and
only if the directed graph is quasi-strongly connected. We also provide a
closed form expression for the global consensus, showing that the effect of
delays is, in general, to introduce a bias in the final decision. The closed
form expression is also useful to modify the consensus mechanism in order to
get rid of the bias with minimum extra complexity.Comment: Conference paper. Journal version submitted to IEEE Transactions on
Signal Processing, January 10, 2007. Paper accepted for the publication on
the VIII IEEE Workshop on Signal Processing Advances in Wireless
Communications, (SPAWC 2007), January 22, 200
Coordination and Privacy Preservation in Multi-Agent Systems
This dissertation considers two key problems in multi-agent systems: coordination (including both synchronization and desynchronization) and privacy preservation.
For coordination in multi-agent systems, we focus on synchronization/desynchronization of distributed pulse-coupled oscillator (PCO) networks and their applications in collective motion coordination. Pulse-coupled oscillators were originally proposed to model synchronization in biological systems such as flashing fireflies and firing neurons. In recent years, with proven scalability, simplicity, accuracy, and robustness, the PCO based synchronization strategy has become a powerful clock synchronization primitive for wireless sensor networks. Driven by these increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synchronization from any initial condition are very sparse. In our work, we address global PCO synchronization from an arbitrary phase distribution under chain or directed tree graphs. More importantly, different from existing global synchronization studies on decentralized PCO networks, our work allows heterogeneous coupling functions and perturbations on PCOs\u27 natural frequencies, and our results hold under any coupling strength between zero and one, which is crucial because a large coupling strength has been shown to be detrimental to the robustness of PCO synchronization to disturbances.
Compared with synchronization, desynchronization of PCOs is less explored. Desynchronization spreads the phase variables of all PCOs uniformly apart (with equal difference between neighboring phases). It has also been found in many biological phenomena, such as neuron spiking and fish signaling. Recently, phase desynchronization has been employed to achieve round-robin scheduling, which is crucial in applications as diverse as media access control of communication networks, realization of analog-to-digital converters, and scheduling of traffic flows in intersections. In our work, we systematically characterize pulse-coupled oscillators based decentralized phase desynchronization and propose an interaction function that is more general than existing results. Numerical simulations show that the proposed pulse based interaction function also has better robustness to pulse losses, time delays, and frequency errors than existing results.
Collective motion coordination is fundamental in systems as diverse as mobile sensor networks, swarm robotics, autonomous vehicles, and animal groups. Inspired by the close relationship between phase synchronization/desynchronization of PCOs and the heading dynamics of connected vehicles/robots, we propose a pulse-based integrated communication and control approach for collective motion coordination. Our approach only employs simple and identical pulses, which significantly reduces processing latency and communication delay compared with conventional packet based communications. Not only can heading control be achieved in the proposed approach to coordinate the headings (orientations) of motions in a network, but also spacing control for circular motion is achievable to design the spacing between neighboring nodes (e.g., vehicles or robots).
The second part of this dissertation is privacy preservation in multi-agent systems. More specifically, we focus on privacy-preserving average consensus as it is key for multi-agent systems, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual nodes (agents) to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In our work, we propose a novel average consensus algorithm for time-varying directed graphs which can protect the privacy of participating nodes\u27 initial states. Leveraging algorithm-level obfuscation, the algorithm does not need the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can guarantee privacy of participating nodes without compromising the accuracy of consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable privacy through compromising accuracy in obtained consensus value. The approach is able to protect the privacy of participating nodes even in the presence of multiple honest-but-curious nodes which can collude with each other
Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems
In this paper we propose a decentralized sensor network scheme capable to
reach a globally optimum maximum likelihood (ML) estimate through
self-synchronization of nonlinearly coupled dynamical systems. Each node of the
network is composed of a sensor and a first-order dynamical system initialized
with the local measurements. Nearby nodes interact with each other exchanging
their state value and the final estimate is associated to the state derivative
of each dynamical system. We derive the conditions on the coupling mechanism
guaranteeing that, if the network observes one common phenomenon, each node
converges to the globally optimal ML estimate. We prove that the synchronized
state is globally asymptotically stable if the coupling strength exceeds a
given threshold. Acting on a single parameter, the coupling strength, we show
how, in the case of nonlinear coupling, the network behavior can switch from a
global consensus system to a spatial clustering system. Finally, we show the
effect of the network topology on the scalability properties of the network and
we validate our theoretical findings with simulation results.Comment: Journal paper accepted on IEEE Transactions on Signal Processin
Consensus-based control for a network of diffusion PDEs with boundary local interaction
In this paper the problem of driving the state of a network of identical
agents, modeled by boundary-controlled heat equations, towards a common
steady-state profile is addressed. Decentralized consensus protocols are
proposed to address two distinct problems. The first problem is that of
steering the states of all agents towards the same constant steady-state
profile which corresponds to the spatial average of the agents initial
condition. A linear local interaction rule addressing this requirement is
given. The second problem deals with the case where the controlled boundaries
of the agents dynamics are corrupted by additive persistent disturbances. To
achieve synchronization between agents, while completely rejecting the effect
of the boundary disturbances, a nonlinear sliding-mode based consensus protocol
is proposed. Performance of the proposed local interaction rules are analyzed
by applying a Lyapunov-based approach. Simulation results are presented to
support the effectiveness of the proposed algorithms
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