Method for compressing sensor network data based on optimal order estimation and distributed clustering

Abstract

The invention relates to a method for compressing sensor network data based on optimal order estimation and distributed clustering. The existed data compressing method has low efficiency. The method utilizes the space-time correlation of the data acquired by the sensing node, and determines the initial data group numbers to be transmitted by the system by introducing the optimized stage estimation, thereby not only obtaining the effective correlation factor, but also avoiding producing redundancy factor; and on the other hand, executes clustering process on the network by using cluster as a unit processing node, so that not only the node data processing efficiency of the base station can be improved, but also the capacity that the base station can rapidly locate the position with abnormalvalues or abnormal nodes can be improved. The method provided by the invention is suitable for a real time environment control system based on the wireless sensor network, and can efficiently compress the data of the wireless sensor network, and efficiently reduce the average energy consumption of the node.

Description

technical field [0001] The invention belongs to the technical field of data compression, and relates to a sensor network data compression method based on optimal order estimation and distributed clustering. Background technique [0002] In a monitoring system based on wireless sensor networks (WSNs), each sensor node collects local information around itself, processes it and transmits it to the sink node, and the sink node summarizes the local data collected by all nodes to obtain the overall information of the region of interest. In a wireless sensor network, due to the influence of various factors such as background noise, node failure, wireless communication instability and energy constraints, the sensory data information acquired, processed and transmitted by nodes often has certain errors and certain errors. degree of uncertainty, however in some applications a certain amount of error is usually allowed. That is, under the premise of ensuring the application requiremen...

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Problems solved by technology

However, this type of algorithm still has some shortcomings. For example, when estimating the predicted correlation coefficient, there is no clear definition of the number of groups of original data to be transmitted. If the node is allowed to transmit too much original data, it will inevitably consume a lot of energy of the node. Not only is it not conducive to prolonging the service life of the node, but it will also increase the dimension of the correlation coefficient to be calculated at the base station, that is, the introduction of redundant correlation coefficients, so that the calculation amount of the base station is too large, which in turn affects the response time and data accuracy of the system ; If the number of original data groups transmitted by the node is too small, the base station cannot calculate enough correlation coefficients, and the predicted data will hardly meet the accuracy requirements, resulting in reduced system availability. Therefore, how to effectively define the amount of original data to be transmitted is a problem worthy of in-depth discussion and solution

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