Research Of Data Aggregation Of Internet Of Things Based On SDN

There are many repeated and redundant parts in the data generated and sent by Io T equipment.Transmitting this data reduces the network lifetime and increases the network traffic.Data aggregation can reduce unnecessary data transmission,improve network transmission efficiency,reduce energy consumption of energy and resource-constrained devices,and extend network life.In this paper,the following researches are carried out from the two aspects of the routing structure and pr ocessing algorithm of data aggregation of Io T.(1)The aggregation gateway layer is proposed,and a SDN-based Io T data aggregation architecture and rule algorithm are designed.For sensor nodes with limited energy,the LEACH algorithm is improved,and the remaining energy of the node and its distance from the gateway are considered for clustering.The cluster heads collect the data sent by the nodes in the cluster during the waiting time control ed by SDN and aggregates it.If the data is not received,the unbiased gray prediction model is used to predict the missing data,and the predicted data is aggregated and then sent to the gateway node;for smart device with unlimited energy,the aggregation gateway aggregates the redundant data received during the aggregation time and then sends the result to the network layer.The simulation analysis shows that compared with the architectures of ordinary gateways and OF-GW gateways,the designed architecture has improved the network life by about 32.6%,and the amount of data packets sent by the perception layer to the network layer has been reduced by about 6.1% to 55.1%;compared with the LEACH algo rithm,the designed algorithm is reduced average latency of data aggregation by about24.1%,while avoiding data loss and improving the accuracy of data aggregation.(2)Compressed sensing algorithm is selected as the aggregation processing algorithm under the SDN-based Io T data aggregation architecture,but the compressed sensing aggregated data needs to use reconstruction algorithm to recover the original data.A Two Stagewise Weak Selection-Based Subspace Pursuit(TSWSP)algorithm is proposed,which does not need to know the spa rsity of the signal in advance.The first weak selection adaptively selects the initial atom candidate set,and the second weak selection adaptively culls the wrong atoms that may have been previously selected from the current atom support set,and finally it selects a plurality of related atoms from the curren t atom candidate set to join the atom support set by backtracking.The simulation analysis shows that the proposed algorithm can accurately reconstruct one-dimensional random signals and two-dimensional image signals with unknown sparsity,and it has high stability;compared with OMP,SWOMP,BAOMP,SAMP and SP algorithm,the mean-square error is reduced by approximately 60.5% to99.1%,and the peak signal-to-noise ratio is increased by approximately 2.1%to 34.3%.