The Research Of Data Prediction Mechanism In Perception-Associated Wireless Sensor Networks

With the rapid development of micro-electromechanical systems,wireless communication technology and low-power embedded technology,wireless sensor network has gradually become a competitive information perceiving and processing technology which has been widely used in military information detecting,smart home,medical health,severe environment monitoring and other fields.With long working of sensor network nodes,part of sensor nodes fail to work which lowers the performance of the network service,then how to make the save the energy of wireless sensor network nodes has become a hot topic at home and abroad scholars.Due to sensor nodes' dense deployments in the network,a lot of redundant data transmission between nodes will cause a certain degree of data collision,so that the data is invalid and wasting energy transmission;in addition,the sensor node consists of six building blocks which the data transmission block consume most energy.In order to reduce the amount of network data transmission and save energy,data prediction mechanism is an effective solution.The data predicting accuracy is an important criterion to measure the effectiveness of data prediction mechanism,thereby improving the accuracy of prediction algorithm node has important theoretical significance and practical value.The paper is set from the two indicators of data prediction accuracy and network energy efficiency,we solve the single node's noise problem by Markov chain amending procedure on temporal dimension,and solve multiple nodes' reasonable clustering on by Markov field on spatial dimension and amend the prediction data using grey correlation degree,the main job of the paper is:(1)This paper has researched the single node with noise prediction data,we propose a data prediction algorithm(MC-DP)based on Markov chain.The proposed algorithm firstly improves the smoothness of sensed data sequence by the weakening operation,then predicts the data sequence which shall be sent to the sink using discrete grey model.In case that the predictive data does not satisfy the data accuracy requirement,it is improved with the amending process based on Markov chain.The experimental results show that MC-DP algorithm has higher prediction accuracy rate and lower data error rate for the predictive data sequence,and thus the proposed algorithm can save more energy for nodes in the network.(2)This paper has researched the multi-node data predicting problems with noise,and proposed a data prediction algorithm(GR-DP)based on grey correlation degree.Firstly,this algorithm de-noises the data of sensor nodes using Markov random field model and then computing the grey correlation degree between cluster member nodes and cluster head node in the same cluster and normalize the grey correlation degree,then repairing the predicting parameter of cluster head,and the cluster head nodes'prediction parameters are sent to the sink node,in the last,the system executes the data prediction between cluster head node and sink node and repairs the data when the predicting data is not correct.The Experimental results show that GR-DP algorithm has higher prediction accuracy and higher network energy efficiency,using the prediction algorithm makes sensor nodes can save more energy.