Multi-stage hierarchical clustering spatial correlation temperature sensing data redundancy removal method

Abstract

The invention discloses a multi-stage hierarchical clustering spatial correlation temperature sensing data redundancy removal method, which comprises the following steps of: step 1, acquiring a largeamount of temperature sensing data acquired by a temperature sensor network, improving a k-Means method by using an Euclidean distance and a Pearson distance on a Sink node, performing node similarityanalysis on the node according to node position coordinates, and solving a redundant node cluster; step 2, performing similarity judgment on the data in the cluster by using a Gaussian mixture clustering method at a cluster head CHs node of redundant node clustering, and further performing data redundant clustering on the nodes in the cluster; step 3, after the data redundancy cluster is obtained, randomly weighting the data in the data redundancy cluster to obtain a final redundancy removal result; and step 4, transmitting the temperature data subjected to redundancy removal to the Sink node. According to the multi-stage hierarchical clustering spatial correlation temperature sensing data redundancy removal method, the redundant nodes can be judged more accurately, so that the judgment of redundant data is more accurate, and the result error after redundancy removal is smaller.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a multi-stage hierarchical clustering spatial correlation temperature sensing data de-redundancy method. Background technique [0002] Wireless Sensor Networks (WSNs) are deployed in an area to monitor physical phenomena such as temperature, humidity, and seismic events. In order to obtain accurate information of the environment or events, a large number of sensor nodes are deployed to collect data and report data to the aggregation node Sink in a high-frequency manner. The data generated by sensor nodes usually have high spatial-temporal correlation and contain a lot of redundant data. At the same time, transmitting redundant data will cause unnecessary energy consumption. Therefore, how to reduce the energy consumption of WSNs redundant data transmission and prolong the life of WSNs is a very important issue. [0003] By studying the spatio-temporal correlat...

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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a multi-stage method for judging redundant nodes only based on node positions in the prior art, which has the defect of inaccurate judgment of redundant nodes due to insufficient judgment conditions of redundant nodes. Hierarchical clustering spatial correlation temperature-aware data de-redundancy method

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