传感器基础知识
成本问题
传感器一般具有部署数量大、覆盖区域广等特点，所以要尽可能降低单个传感器的成本，而降低成本则需要超大规模集成电路 (VLSI) 以及微机电系统科技 (MEMS technology) 等技术进一步的发展。
性能评估方法
无线传感器网络是一种特殊的自组织网络（Ad hoc networks），和自组织网络一样面临着能量有限（energy constraints）以及路由选择（routing）的挑战，其中能量有限（energy constraints）是无线传感器最大的挑战。
有很多指标可以用来评估传感器网络的性能情况，其中主要有：
- Energy efficiency/system lifetime. The sensors are battery operated, rendering energy a very scarce resource that must be wisely managed in order to extend the lifetime of the network[2].
- Latency. Many sensor applications require delay-guaranteed service. Protocols must ensure that sensed data will be delivered to the user within a certain delay. Prominent examples in this class of networks are certainly the sensor-actuator networks.
- Fault tolerance. Robustness to sensor and link failures must be achieved through redundancy and collaborative processing and communication.
- Scalability. Because a sensor network may contain thousands of nodes, scalability is a critical factor that guarantees that the network performance does not significantly degrade as the network size (or node density) increases.
- Transport capacity/throughput. Because most sensor data must be delivered to a single base station or fusion center, a critical area in the sensor network exists, whose sensor nodes must relay the data generated by virtually all nodes in the network. Thus, the traffic load at those critical nodes is heavy, even when the average traffic rate is low. Apparently, this area has a paramount influence on system lifetime, packet end-to-end delay, and scalability.
提高传输效率的方法
可以利用下面几种方式来提高传感器的能量利用效率[1]：
- 首先，可以在集群中应用分布式源编码的形式进行数据压缩，以减少要传输的数据包数量。First, data compression in the form of distributed source coding is applied within a cluster to reduce the number of packets to be transmitted.
- 其次，数据中心化的属性使传感器节点的标识 (例如地址) 变得用处不大，然而在很多场景中我们比较关注某些区域的数据特点，这时就要去除中心化，利用分布式处理数据的方法。Second, the data-centric property makes an identity (e.g., an address) for a sensor node obsolete. In fact, the user is often interested in phenomena occurring in a specified area[3], rather than in an individual sensor node.
- 此外，也可以利用广播树和多播树策略来提高能量利用效率（该方法缺点：会导致较高的计算复杂度）。Another strategy to increase energy efficiency is to use broadcast and multicast trees.
- 最后，引入传感器睡眠机制减少电量消耗。The exploitation of sleep modes is imperative to prevent sensor nodes from wasting energy in receiving packets unintended for them.
建模
一般对传感器建模时考虑传感器可能处于四种状态：传输信号(transmission), 信号检测(reception), 信号接收(listening), 睡眠(sleeping)。其中，
- Transmission: processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.
- Reception: Low-noise amplifier, downconverter oscillator, filtering, detection, decoding, error detection, and address check; reception even if a node is not the intended receiver.
- Listening: Similar to reception except that the signal processing chain stops at the detection.
- Sleeping: Power supply to stay alive.
下一代传感器技术
传感器的协作问题
因为有时候需要对一个移动的目标进行数据采集，比如说移动目标地理位置信息，很多时候要想准确得到这样的信息需要多种不同传感器之间协作，因为单个传感器采集的数据有时会出错以及误报。现在可以采用分布式计算技术来解决这一问题：
These capabilities are now being extended to include high-speed wireless and fiber networking with distributed computing. As the Internet protocol (IP) technologies continue to advance in the commercial sector, the military can begin to leverage IP formatted sensor data to be compatible with commercial high-speed routers and switches. Sensor data from theater can be posted to high-speed networks, wireless and fiber, to request computing services as they become available on this network. The sensor data are processed in a distributed fashion across the network, thereby providing a larger pool of resources in real time to meet stringent latency requirements. The availability of distributed processing in a grid-computing architecture offers a high degree of robustness throughout the network. One important application to benefit from these advances is the ability to geolocate and identify mobile targets accurately from multiaspect sensor data.
目前协作存在的不足：The limitation is with the communication and available distributed computing.
[1] Mahgoub I, Ilyas M. Smart dust : sensor network applications, architecture, and design[J]. Journal of Strain Analysis for Engineering Design, 2006, 37(1):21-31.
[2] Ephremides A. Energy concerns in wireless networks[J]. Wireless Communications IEEE, 2002, 9(4):48-59.
[3] Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion:a scalable and robust communication paradigm for sensor networks[C]// ACM, 2000:56-67.