WIENER PREDICTION FOR ENVIRONMENTAL MONITORING IN WIRELESS SENSOR NETWORK OF CLUSTER STRUCTURE
Environmental monitoring is very important applications of wireless sensor net-works (WSNs).The lives of WSNs are of several months, or even years. However, the inherent restriction of energy carried within the battery of sensor nodes brings an extreme difficulty to obtain a satisfactory network lifetime, which becomes a bottleneck in scale of such applications in WSNs. Proposed novel frame work of data prediction, can apply in WSN to simultaneously achieve accuracy and efficiency of the data processing in clustered architectures. The main aim of the framework is to reduce the communication cost while guaranteeing the data processing and data prediction accuracy. In this framework, data prediction is achieved by implementing the dual wiener prediction algorithm with optimal step size by minimizing the mean-square derivation (MSD), in a way that the cluster heads(CHs) can obtain a good approximation of the real data from the sensor nodes. On this basis, a centralized Principal Component Analysis (PCA) technique is utilized to perform the compression and recovery for the predicted data on the CHs and the sink, separately in order to save the communication cost and to eliminate the spatial redundancy of these used data about environment . All errors generated in these processes are finally evaluated theoretically, which come out to be controllable. Based on the theoretical analysis, designing is possible a number of algorithms for implementation. Simulation results by using the real world data demonstrate that our frame work provides a cost-effective solution to such as environmental monitoring applications in cluster based WSNs..