Distributed optimization in sensor networks
Distributed optimization in sensor networks
复制标题
DOI：
10.1145/984622.984626
复制
发表时间：
2004-04
期刊：
影响因子：
--
通讯作者：
M. Rabbat;R. Nowak
中科院分区：
文献类型：
--
作者：
M. Rabbat;R. Nowak
无线传感器网络能够在空间和时间上收集大量数据。通常，最终目标是从这些数据中得出参数或函数的估计值。本文研究了用于“网络内”数据处理的一般分布式算法，从而消除了将原始数据传输到中心点的需求。这可以大大减少获得准确估计所需的通信和能量。我们认为的估计问题表示为涉及所有传感器节点数据的成本函数的优化。分布式算法基于增量优化过程。参数估计通过网络循环，并在每个节点根据其本地数据对估计值进行少量调整。应用了从增量亚级别优化理论的结果，我们表明，对于一系列估计问题，分布式算法会收敛到围绕全球最佳值的AN /SPL EPSI /-BALL内。此外，特定准确级别所需的数字增量步骤的界限提供了对估计绩效和通信开销之间的权衡的见解。在许多现实的情况下，在能源和通信方面，分布式算法比集中式估计方案要高得多。通过模拟应用，源估计，源定位，聚类分析和密度估计，通过模拟应用来验证该理论。
Wireless sensor networks are capable of collecting an enormous amount of data over space and time. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. The distributed algorithms are based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data. Applying results from the theory of incremental subgradient optimization, we show that for a broad class of estimation problems the distributed algorithms converge to within an /spl epsi/-ball around the globally optimal value. Furthermore, bounds on the number incremental steps required for a particular level of accuracy provide insight into the trade-off between estimation performance and communication overhead. In many realistic scenarios, the distributed algorithms are much more efficient, in terms of energy and communications, than centralized estimation schemes. The theory is verified through simulated applications in robust estimation, source localization, cluster analysis and density estimation.