通信工程文献翻译——摘要：

分布式无线传感器网络可以用于环境感知，目标跟踪，数据收集等。它通过特定的传感器收集数据，解决包括估计和探测在内的问题。由于分布式无线传感器网络（DWSN）的特点，希望使用信息沟通和融合成本最低的算法。解决该问题的一些最近的方法都详尽的在本文中。DWSN着重点在于分布式估计和最优化算法，它与标准的集中式构架相比，具有功耗低和传输要求低的特点。

1 引言

超大规模集成电路（VLSI）和通信技术的最新发展，促进了分布式无线传感器网络在环境感知、目标追踪、数据收集以及其他应用程序方面的应用。无线传感器网络是由一些小的低功耗的设备组成的，这些低功耗的设备由具有机上处理和无线通信能力的微传感器集成。不像以前架构的传感器系统，分布式无线传感器网络允许分布式传感和信号处理[1]。因为传感器是电池供电而且它的无线通信带宽有限，所以这些低功耗的设备符合分布式无线传感器网络要求的。因此从通信传感器的能源消耗占主导地位的体制到传感和计算占主导地位体制的转换是合理的。

在一个标准的DWSN结构中，根据该DWSN的应用，系统可能会进行估计，检测，分类定位和跟踪任务。这些任务是由每个传感器独立地工作，并通过本地存储记录数据。传感器将在局部处理数据，减少其复杂性，然后传送到数据处理中心，完成进一步加工。该中心可以从独立的各个节点的数据得出整个网络的一个数据值。对于某些任务，可以从收集到的数据中作出决定，节点也可能发送个体决定（硬决定）或它们的测量值（软决定）。在任何情况下，数据处理中心将负责融合进程，使整个网络达成一项决定。

在估计问题上，提出了一些利用测量事件物理特性的方法。对许多事件最重要的是综合在不同节点处的测量值的相关性。在一个空间域（即音频信号功率，压力，温度等）内，事件的这个性质在估计数量变得平滑的地方被观测到。利用此特点，最新的方法是分布式估计，努力减少计算量，通过减少信息处理中心所收集的数据冗余，得到融合估计要求的通信量。

另外，有关估计的问题是估计的准确性。这个准确性受事件测量时的状况影响，包括：传感器的精度，传感器的空间密度和通信带宽。因此，在不同的地点不同区域内可以通过调整所需的估计精度进行部署，监测。

在任何情况下，网络容量限制了流过网络节点的信息量。网络容量取决......

Abstract— Distributed wireless sensor networks have been proposed as a solution to environment sensing, target tracking,data collection and others. These problems involve estimation or detection problems using data collected by individual sensors.

Due to the characteristics of the DWSN, it is desired to use algorithms that will allow for low-cost information communication and fusion. Some of the recent approaches to these problems are detailed in this paper. There is a heavy focus on distributed estimation and optimization algorithms that feature lower power consumption and communication requirements than the standard centralized architecture.

I. INTRODUCTION

Recent advances in VLSI and communication technology have promoted the deployment of distributed wireless sensor networks (DWSN) for environment sensing, target tracking,data collection and other applications. These networks are composed generally by small, low-power devices that integrate micro-sensing and actuation with on-board processing and wireless communication capabilities. Unlike previous architectures for sensor networks, DWSNs allow for distributed sensing and signal processing [1]. These qualities of a DWSN are desirable since the sensors are battery-powered and have limited wireless communication bandwidth. Therefore,it is reasonable to shift the energy consumption in sensors from a communications-dominating scheme to a sensing and computation-dominating scheme.In a normal DWSN architecture, the system may perform estimation, detection, classification localization and tracking tasks, depending on the problem the DWSN is applied to.

These tasks will operate individually on each sensor by using signals recorded locally. Sensors will locally process the data to reduce its dimensionality and then tranmit the data to a processing center, where further processing may be done. The center may also fuse the data from the individual nodes to arrive at a single data value for the whole network. For some of the tasks, decisions are made from the collected data, and the nodes may send individual decisions (hard decisions) or their......