Distributed routing in wireless sensor networks using energy welfare metric
Information Sciences: an International Journal
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Diffusion least-mean squares with adaptive combiners: formulation and performance analysis
IEEE Transactions on Signal Processing
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
Distributed estimation over complex networks
Information Sciences: an International Journal
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
Incremental Adaptive Strategies Over Distributed Networks
IEEE Transactions on Signal Processing
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The topological structure of sensor network possesses distinctive and interesting characteristics that are important for many applications. In the previous work by Liu et al. (Y. Liu, C. Li, W.K.S. Tang, Z. Zhang, Distributed estimation over complex networks, Inform. Sci. 197(8) (2012) 91-104) the effects of network topology on distributed estimation have been addressed. In this paper, we further focus on the optimal topological design of sensor networks, which targets for improving the performance of distributed estimation. Based on spectral analysis, it is shown that this design problem is equivalent to finding an optimal topology that maximizes the eigenratio of the second smallest and the largest eigenvalues of the respective network Laplacian matrix. To tackle this optimization problem, a computational algorithm combining a local greedy algorithm and tabu search is proposed, in which the constraint on the distance of two communicated sensors is incorporated. As shown in the numerical simulations, the proposed algorithm outperforms other optimization strategies in the viewpoints of accuracy, robustness and complexity. Consequently, the quality of distributed estimation can be improved by obtaining a better network topology.