Abstract—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. Index Terms—Distributed consensus, distributed estimation, dynamical systems, sensor networks. I.

We study the role of information feedback for the problem of distributed signal tracking/estimation using a sensor network with a fusion center. Assuming that the fusion center has sufficient energy to reliably feed back its intermediate estimates, we show that the sensors can substantially reduce their power consumption by using the feedback information in a manner similar to the stochastic approximation scheme of Robbins-Monro. For the problem of tracking an autoregressive source or estimating an unknown parameter, we quantify the total achievable power saving (as compared to the distributed schemes with no feedback), and provide numerical simulations to confirm the theoretical analysis. 1. PROBLEM FORMULATION Consider the problem of tracking a signal source or estimating an unknown parameter by a wireless sensor network with a fusion center

In this work we review some of the most recent in-network computation capabilities that can be used in sensor networks to alleviate the information traffic from the sensors towards the sink nodes. More specifically, after briefly reviewing distributed average consensus techniques, we will concentrate on consensus mechanisms based on self-synchronization of coupled dynamical systems, initialized with local measurements. We will show how to achieve globally optimal distributed detection and estimation through minimum exchange of information between nearby nodes in the case where the whole network observes one common event. 1.

Abstract- In this paper, we outline an approach to improve the lifespan of a wireless sensor network by introducing a variant to standard sleep synchronization protocols. A multilayered architecture is used. To ensure even higher scalability and lower message size in any particular layer, number of layers is limited to four and each layer is broken into grids. Each grid acts a localized network where data aggregation and lifetime maximization algorithms are being run. In standard sleep protocols like GAF, each grid must have one of its nodes in active state. Our sleep protocol considers one node per grid to be in the idle listening state called the ‘doze ’ state for a fixed interval of time. Thus we propose a three state proactive algorithm in the form of the Sleep Doze Coordination (SDC) protocol to lower the duty cycle of the each sensor node and maximize the network lifespan with lower power consumption. Node buffers are provided to bring about higher data accuracy and lossless network operation. When node buffer gets filled to its capacity by data messages from the lower layer, it signals the ‘dozing ’ node to transit to the active state. Thus the node does not have to remain active throughout its ‘on ’ period and its overall lifespan increases for a given amount of energy. Results indicate that near-optimal performance of SDC is achieved when buffer size is large enough to hold 25 data messages. SDC increases network lifetime by approximately 20 % over previous protocols like GAF and S-DMAC. Keywords- wireless sensor networks, duty cycle, SDC protocol, queueing discipline, M/M/1 queue, buffering. W I.