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Distributed Relaxation on Augmented Lagrangian for Consensus based Estimation in Sensor Networks

机译：传感器网络中基于共识的估计的增强拉格朗日分布松弛

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This paper presents several new distributed algorithms to solve consensus based estimation problems in a decentralized way by adopting the well-known relaxation methods Jacobi, Gauss-Seidel and successive over-relaxation within a sensor network. In distributed estimation, all nodes collaborate to estimate the signals emitted from some common sources, employing iterative processing with one-hop data exchange. Consequently, the Jacobi-based consensus estimation algorithm produces a considerable communication effort due to its parallel processing. On the contrary, significant overhead can be saved by the Gauss-Seidel based consensus estimation algorithm with sequential update and exchange of local information. Additionally, both distributed algorithms can be accelerated by successive over-relaxation, resulting in further reduction of the communication effort for the distributed estimation. The evaluation of all the algorithms has been carried out in presence of both ideal and erroneous inter-node links in a randomly generated network. Moreover, the influence of the network topology on the distributed estimation has also been investigated, and the simulative results indicate that a network with low connectivity is preferred by the proposed algorithms.
机译：本文提出了几种新的分布式算法，通过采用著名的松弛方法Jacobi，Gauss-Seidel和传感器网络中的连续过度松弛来以分散方式解决基于共识的估计问题。在分布式估计中，所有节点协作进行估计，并使用带有单跳数据交换的迭代处理来估计从某些常见源发出的信号。因此，基于雅可比的共识估计算法由于其并行处理而产生了大量的通信工作。相反，基于Gauss-Seidel的共识估计算法可以通过顺序更新和交换本地信息来节省大量开销。另外，可以通过连续的过度松弛来加速这两种分布式算法，从而进一步减少了用于分布式估计的通信工作量。所有算法的评估都是在随机生成的网络中同时存在理想和错误的节点间链接的情况下进行的。此外，还研究了网络拓扑结构对分布式估计的影响，仿真结果表明，所提出的算法优选低连接性的网络。

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