Exploiting the Superposition Property of Wireless Communication for Max-Consensus Problems in Multi-Agent Systems

This paper presents a consensus protocol that achieves max-consensus in multi-agent systems over wireless channels. Interference, a feature of the wireless channel, is exploited: each agent receives a superposition of broadcast data, rather than individual values. With this information, the system endowed with the proposed consensus protocol reaches max-consensus in a finite number of steps. A comparison with traditional approaches shows that the proposed consensus protocol achieves a faster convergence.Comment: Submitted for IFAC Workshop on Distributed Estimation and Control in Networked System

Efficient Consensus-based Formation Control With Discrete-Time Broadcast Updates

This paper presents a consensus-based formation control strategy for autonomous agents moving in the plane with continuous-time single integrator dynamics. In order to save wireless resources (bandwidth, energy, etc), the designed controller exploits the superposition property of the wireless channel. A communication system, which is based on the Wireless Multiple Access Channel (WMAC) model and can deal with the presence of a fading channel is designed. Agents access the channel with simultaneous broadcasts at synchronous update times. A continuous-time controller with discrete-time updates is proposed. A proof of convergence is given and simulations are shown, demonstrating the effectiveness of the suggested approach.Comment: Submitted to CDC 201

Distributed Bio-inspired Humanoid Posture Control

This paper presents an innovative distributed bio-inspired posture control strategy for a humanoid, employing a balance control system DEC (Disturbance Estimation and Compensation). Its inherently modular structure could potentially lead to conflicts among modules, as already shown in literature. A distributed control strategy is presented here, whose underlying idea is to let only one module at a time perform balancing, whilst the other joints are controlled to be at a fixed position. Modules agree, in a distributed fashion, on which module to enable, by iterating a max-consensus protocol. Simulations performed with a triple inverted pendulum model show that this approach limits the conflicts among modules while achieving the desired posture and allows for saving energy while performing the task. This comes at the cost of a higher rise time.Comment: 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

In-network Sparsity-regularized Rank Minimization: Algorithms and Applications

Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed sparsity-regularized rank minimization over networks, when the nuclear- and $\ell_1$-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits when centralized processing is viable, non-separability of the singular-value sum challenges its distributed minimization. To overcome this limitation, an alternative characterization of the nuclear norm is adopted which leads to a separable, yet non-convex cost minimized via the alternating-direction method of multipliers. The novel distributed iterations entail reduced-complexity per-node tasks, and affordable message passing among single-hop neighbors. Interestingly, upon convergence the distributed (non-convex) estimator provably attains the global optimum of its centralized counterpart, regardless of initialization. Several application domains are outlined to highlight the generality and impact of the proposed framework. These include unveiling traffic anomalies in backbone networks, predicting networkwide path latencies, and mapping the RF ambiance using wireless cognitive radios. Simulations with synthetic and real network data corroborate the convergence of the novel distributed algorithm, and its centralized performance guarantees.Comment: 30 pages, submitted for publication on the IEEE Trans. Signal Proces

Distributed signal processing and optimization based on in-network subspace projections

We study distributed optimization and processing of subspace-constrained signals in multi-agent networks with sparse connectivity. We introduce the first optimization framework based on distributed subspace projections, aimed at minimizing a network cost function depending on the specific processing task, while imposing subspace constraints on the final solution. The proposed method hinges on (sub)gradient optimization techniques while leveraging distributed projections as a mechanism to enforce subspace constraints in a cooperative and distributed fashion. Asymptotic convergence rates to optimal solutions of the problem are established under different assumptions (e.g., nondifferentiability, nonconvexity, etc.) on the objective function. We also introduce an extension of the framework that works with constant step-sizes, thus enabling faster convergence to optimal solutions of the optimization problem. Our algorithmic framework is very flexible and can be customized to a variety of problems in distributed signal processing. Finally, numerical tests on synthetic and realistic data illustrate how the proposed methods compare favorably to existing distributed algorithms