Quantized incremental algorithms for distributed optimization
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Conference Proceeding: Distributed least square support vector regression for environmental field estimation[show abstract] [hide abstract]
ABSTRACT: A distributed approach to monitoring the environmental field function with mobile sensor networks is presented in this paper. With this approach, a mobile sensor network is capable to estimate a model of field functions in real-time. This approach consists of two stages, a field function learning stage and a locational optimising stage. A distributed least square support vector regression (LS-SVR) is developed for the field function learning stage. On the locational optimising stage, a gradient based method: centroidal Voronoi tessellation (CVT) is used to allocate each sensor node's position. These two stages are running alternately in a loop so that the field function learning stage can keep updating the field function with new sensor readings resulted from the locational optimising stage, and simultaneously, the locational optimising stage can relocate sensor nodes according to a more accurate field function model. Eventually, the field function is estimated and the sensor nodes are distributed based on the estimated model. The simulation results given in this paper show the effectiveness of this approach.Information and Automation (ICIA), 2011 IEEE International Conference on; 07/2011
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ABSTRACT: Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed EM algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach.Sensors 01/2011; 11(6):6297-316. · 1.95 Impact Factor
- IEEE Transactions on Signal Processing 01/2011; 59:3863-3875. · 2.81 Impact Factor