Information Processing Research In Wireless Sensor Network

Wireless Sensor Networks (WSN) is a self-organized distributed intelligent system comprising low-cost, low-power, multifunctional sensor nodes that are small in size and capable of wireless communication in short distances. WSN, which integrates the technologies of sensor micro electro mechanical, wireless communications and distributed computation, is a novel mode of computing and a hotspot of information technology after internet. It will change the way that human recognize and communicate with the physical nature. Sensor network is a new research area of computer science and technology and has a wide application in the future. Both academia and industries are very interested in it.Wireless Sensor Networks is an information-centric system. The basic role of the WSN is to collect information of monitored objects in the environment, and then to process and transmit them to the sink node. Any application of wireless sensor networks is related to data management and information processing. Information processing in WSN includes data compression algorithms, distributed database storage and query strategies, in-network data fusion or aggregation and so on. The main goal of information processing in WSN is to manage the sensing data collected from the environment with limited resources and energy save. So that, the users can manages and process the sensing data as in the same convenient way as processing on the normal database without consider the detail of WSN.However, the characteristics of WSN bring great challenges to the information processing in WSN, such as the ultra large number of sensor nodes, dense deployment, frequently changing topology structure, the limited resources, including computation, storage and communication capability, and so on. All these require the information processing algorithms to be energy-efficient, scalable well and robust.This thesis gives an overview of the former work on the information processing approaches in WSN, and studies the related issues of information processing of WSN. The major contribution of this dissertation is stated as follows:1) This thesis proposed a novel distributed wavelet compression algorithm basing on simple lifting factorization for reducing energy consumption in wireless sensor network. This low complexity algorithm canexploit the inherent correlations that exist in or between sensor readings and significantly reduce transmission costs, and then prolong the network lifetime.2) Focusing on the application requirements in environment surveillance, a novel distributed 3D wavelet image compression algorithm was developed. Since surveillance image sequences are often characterized by low motion and high correlation in WSN, Low-complexity Change Detection Algorithm (LCDA) as well as Position Estimation and Compensation Algorithm (PE&CA) was designed to reduce the energy consumption by the way detect and compress interested regions, remove the correlation of the image collected by each sensor.3) Based on the multi-resolution analysis of wavelet, this thesis proposed a novel distributed storage/query method, which distributed data to every sensor node, and constructed a wavelet spatial architecture tree. This approach can store data efficiently and support highly efficient spatio-temporal query.4) The quality of link communication has a significant impact on upper-layer application in wireless sensor networks. We study the problem of fusing decisions transmitted over fading channels in a wireless sensor network. We propose a new likelihood ratio (LR)-based fusion rule which requires only the knowledge of channel statistics. Two suboptimum alternatives are presented in the low or high SNR channel to reduce the energy consumption. Under the identical local sensor and the same channel SNR (singnal noise rate) assumption, we study the fusion statistic in the last.5) Based on modified Apriori algorithm, this thesis proposes a novel node association rule mining algorithm for exploiting the inherent correlations between sensor readings. This approach can help users to manage data efficiently during aggregation, classification, prediction, query, understanding and decision-making, and then reduce computation and communication energy in information processing effectively.In the last chapter, we conclude the dissertation and outlines future research.