Hopfield-Like Neural Nets and Sensor Networks
Neural Processing Letters
Decentralized Synchronization and Estimation in Wireless Networks
NEW2AN '08 / ruSMART '08 Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking
Hopfield neural networks for on-line parameter estimation
Neural Networks
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Mean square convergence of consensus algorithms in random WSNs
IEEE Transactions on Signal Processing
Bio-inspired algorithms for decentralized round-robin and proportional fair scheduling
IEEE Journal on Selected Areas in Communications
Decentralized subspace tracking via gossiping
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Self-aware distributed consensus building for sensor networks
ISRN Communications and Networking
ORACLE: Mobility control in wireless sensor and actor networks
Computer Communications
Journal of High Speed Networks
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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.