Research On The Processing Of Uncertain Data On Wireless Sensor Surveillance Network Environment

Wireless Sensor Network (WSN), which integrates the technologies of sensor, micro-electro-mechanism system (MEMS), wireless communication and distributed computing, is a novel mode of computing and a hotspot of information technology after Internet. It will have a profound influence on many areas in 21st century. Internet changes the way people communicate and exchange, while WSN connects the physical world to the logical information world, and will bring on the revolution of the interacting way between human and nature. WSN is application specific. Most of the applications of WSN have the character of surveillance, so the research of Wireless Sensor Surveillance Network (WSSN) is very significant. Measure errors and network transmission errors cannot be avoided entirety in the applications of WSSN. Furthermore, extreme limited system resources like network bandwidth and battery power in WSSN can only afford sampling data in a discrete manner, while the values of the entities being monitored (e.g. temperature, pressure) is changing constantly. The intrinsic inconsistency or uncertainty of data related in WSSN makes such data uncertain data in nature. Uncertain data offer a new challenge for traditional data processing methods.In WSSN composed of a large number of low-power, short-lived, unreliable sensors, one of the most important design challenges is to obtain long system lifetime, as well as maintain sufficient surveillance to targets. The definition of the general lifetime of system is proposed and a round-based decentralized nodes scheduling scheme is presented, which schedule nodes in each cluster independently, therefore extend the general lifetime of system in differentiated surveillance. Fault-tolerance can be achieved with our scheme by taking nodes status and residual energy into account.The query and update of uncertain data are the foundations of the processing technology of uncertain data. Probabilistic query on uncertain data, which is based on the probabilistic uncertainty model, places confidence to query answers based on the uncertainty intervals and their probability distributions (uncertainty pdfs). By analysing the classification of probabilistic queries and related evaluation methods, an improved evaluation method for Entity-based Nearest Neighbor Query (ENNQ) is proposed. Metrics used to measure the qualities of the results returned by Entity-based Range Queries (ERQ) are proposed based on the notion of information entropy. An entropy-based updating scheme for uncertain data is presented, so as to improve the qualities of queries by the minimum energy overhead.There may be a large amount of clients who need to access sensing data on WSSN environment. Dissemination of uncertain data is another important issue in the processing of uncertain data. Data broadcasting is an effective means for data dissemination method on mobile computing environment. Definition of the mean uncertainty ratio of data is presented and a broadcasting scheme is proposed for uncertain data dissemination, based on the push-based online data broadcast. The demand probability and the uncertainty of data are considered in the process of broadcast scheduling. The effect of transmission errors and multiple broadcast channels are also taken into account in the scheme.The applications of WSSN usually involve a large amount of data. It is significant for the research on data mining on such large volume data. The uncertainty of data on WSSN environment affects the correctness of data mining remarkably, which offers new challenges for traditional data mining methods. The issue of clustering of uncertain data is focused on and a probabilistic density-based clustering algorithm for uncertain data is proposed based on the probability distribution of uncertainty. Effectiveness is improved by taking the probability distribution information in the uncertainty intervals of data into consideration, efficiency is achieved with R-tree and Probability Threshold Index (PTI).