无线传感器网络分布式估计的自适应链路选择策略
Adaptive Link Selection Strategies for Distributed Estimation in Wireless Sensor Networks
摘要：
在这项工作中，我们提出了自适应链路选择策略的分布式估计扩散型无线网络。我们开发了一个基于穷举搜索的链路选择算法和一个稀疏启发的链路选择算法，可以利用网络的拓扑结构与低质量的链接。在基于穷举搜索的算法中，我们选择导致特定节点的最小超额均方误差（EMSE）的邻居集合。在稀疏启发链路选择算法中，引入凸正则化设计了一种稀疏启发链路选择算法。所提出的算法有能力装备扩散型无线网络，并显着提高其性能。仿真结果表明，与现有算法相比，所提算法具有更低的EMSE值和更好的收敛速度，显著提高了网络性能。
英文摘要：
In this work, we propose adaptive link selection strategies for distributed estimation in diffusion-type wireless networks. We develop an exhaustive search-based link selection algorithm and a sparsity-inspired link selection algorithm that can exploit the topology of networks with poor-quality links. In the exhaustive search-based algorithm, we choose the set of neighbors that results in the smallest excess mean square error (EMSE) for a specific node. In the sparsity-inspired link selection algorithm, a convex regularization is introduced to devise a sparsity-inspired link selection algorithm. The proposed algorithms have the ability to equip diffusion-type wireless networks and to significantly improve their performance. Simulation results illustrate that the proposed algorithms have lower EMSE values, a better convergence rate and significantly improve the network performance when compared with existing methods.
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