6. Online Distributed Identification for Wireless Sensor Networks

This chapter introduces an online dual-rate distributed identification framework for wireless sensor networks. Distributed identification is a concept that allows an individual unit to obtain local estimation using part of the data, and the obtained local estimation can then be used as a basis for global estimation. In this chapter, typical architectures of wireless sensor networks will first be introduced, including centralized, decentralized and distributed networks. Then, the online dual-rate distributed identification approach is introduced for wireless sensor networks. Filtering method using only raw observations collected at each sensor is introduced. The preliminary local identification results are then compressed before transmitting to the central station for fusion. At the central station, Bayesian fusion is developed to integrate the compressed local identification results transmitted from the sensor nodes in order to obtain reliable global estimation. As a result, the large identification uncertainty in the local identification results can be substantially reduced. In addition to data compression, a dual-rate strategy for sampling and transmission/fusion is used to alleviate the data transmission burden so that online model updating can be realized efficiently for wireless sensor networks. The computational framework in this chapter will be followed in the next chapter, where specific algorithms for handling asynchronous data and multiple outlier-corrupted data will be introduced.