Work of this paper is based on GAINS sensor nodes.
本文的工作就是在GAINS传感器节点平台上展开的。
The researchers initially used trial and error to place the sensor nodes.
研究人员起初用测试和误差来定位传感器节点。
Sensor nodes are the basic hardware units for building up wireless sensor networks.
传感器节点是构建无线传感器网络的基本硬件单元。
First, the number and type of sensors can be adapted at the individual sensor nodes.
首先，在数量和类型的传感器可以适应在各个传感器节点。
For example, sensor data can be collected from one or more sensor nodes in a network.
例如，可以从网络中的一个或多个传感器节点采集传感器数据。
All these applications and most of the in-network studies require the locations of sensor nodes.
绝大多数的无线传感器网络应用及自身系统研究需要传感器节点的位置。
The most important part of network hardware system in wireless sensor networks is the sensor nodes.
传感器节点是无线传感器网络硬件系统的核心。
This approach meant that the individual sensor nodes could have much less powerful radios, thus saving energy.
这个方法是指单独的传感器节点比强大的无线电耗电少，因此能存储能量。
Random diffusion is widely applied to sensor nodes deployment, for example Gauss diffusion and uniform diffusion.
为实现传感器节点的配置，随机散布方式被广泛地采用。
Wireless sensor network is a multi-hop wireless network which is made up of a lot of distributed smart sensor nodes.
无线传感器网络是由大量智能传感器节点组成的多跳无线网络。
The Interactive profile adds additional lights, enhanced timing, and sensor nodes for interaction with the 3D environment.
Interactive配置文件添加了其他光源、增益和传感器节点，用来与3D环境进行交互。
The wireless sensor network is a multi-hop wireless network which is made up of a lot of distributed smart sensor nodes.
无线传感器网络是由许多分布的智能传感器节点组成的多跳无线网络。
In wireless sensor networks it is necessary to encrypt messages sent among sensor nodes to achieve communication security.
为了实现无线传感器网络中的安全通讯，需要对传感器结点间传递的信息进行加密。
A lot of traditional key management schemes are not suitable for sensor networks for the limited resource in sensor nodes.
由于传感器节点的资源限制，许多传统的密钥管理方案并不适合传感器网络。
The collaborative tracking is implemented through sensor selection, and the result of tracking is proposed among sensor nodes.
协作目标跟踪是通过节点选择来完成的，跟踪的结果在选择的节点之间传输。
A key challenge in wireless sensor networks is to achieve maximal network lifetime with dynamic power management on sensor nodes.
在无线传感器网络中，如何动态地管理能量，最大限度地延长网络的生命周期是一个关键的问题。
The paper introduces the ultralow-power hardware design of wireless sensor nodes for online oscillation signal detection and analysis.
本文介绍了实现在线振动检测和分析的无线传感器节点的超低功耗硬件设计。
A collection of sensor nodes record temperature and battery level readings and send them to a collection node for off-line processing.
一些传感器节点记录温度和电量水平信息并发送给采集节点。
Considering the limited energy and transmission range of sensor nodes in sensor networks, an innovative routing algorithm was proposed.
针对传感器节点能量及传输范围有限等特点，提出了一种基于簇的自适应路由算法。
The sensor nodes must be aware its position to explain 'what happens and where ', to implement the target of the location and tracking.
传感器节点必须准确定位才能说明“在什么位置或区域发生了特定事件”，实现对外部目标的定位和追踪。
But because of resource constraints of sensor nodes, the now mature security technology is not well applied to wireless sensor networks.
然而，由于传感器节点的资源限制，使得现已成熟的安全技术不能很好的应用到无线传感器网络中。
Sensor nodes consist of core module, power module, wireless communication module, data acquisition module, and RS-232 communication module.
传感器节点主要包括核心模块、电源模块、无线通信模块、数据采集模块和RS- 232通信模块。
Constructing the running model of sensor nodes, predicting the remaining energy of its neighbor nodes, the routing selection was optimized.
算法通过建立传感器网络节点运作模型，及相邻节点剩余能量预测机制，优化路由选择。
As the energy of sensor nodes mainly is consumed in transmitting data, reducing the amount of data transmitted can extend the network lifetime.
由于传感器节点的能量主要消耗在传输数据上，所以减少节点数据传输量可以有效延长网络生命期。
Different from other wireless communication systems, wireless sensor networks can not be added generally because of the battery-powered sensor nodes.
与其它的无线通信系统不同，在无线传感器网络中，由于传感器节点由电池供电，一般不能补充。
Wireless sensor networks consist of a large number of low-power, short-lived, unreliable sensor nodes, one of main goals is to prolong network lifetime.
无线传感器网络是由大量低能量、短寿命、不可靠的传感器结点组成的，延长网络寿命是一个主要的目标。
By the mutual cooperation of large number of sensor nodes, sensor network can sense, monitor and gather the information of a given object or environment.
大量传感器节点通过相互之间的分工协作，可实时感知、监测和采集分布区域内的监测对象或周围环境的信息。
Because of the high bandwidth demands of multimedia date, the transmission of raw data collected at sensor nodes will consume a large amount of resources.
因为多媒体数据的高带宽要求，要传输传感器节点采集到的原始数据将会消耗大量的资源。
It is simple to implement with low complexity in communication and computation. No additional hardware or data communication is required for sensor nodes.
算法原理简单，传感器节点无须任何附加硬件或附加数据通信，通信、计算复杂度较低。
It is simple to implement with low complexity in communication and computation. No additional hardware or data communication is required for sensor nodes.
算法原理简单，传感器节点无须任何附加硬件或附加数据通信，通信、计算复杂度较低。
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