Research On Energy Saving Acquisition And Compressed Storage Of Big Data Traffic Based On LoRa

With the continuous development of the Internet of things technology,the requirements of low power consumption and transmission distance of nodes in the industry of the Internet of things need to be improved.And at the same time,the data collected by a large number of nodes will be stored in the database,which will also generate the demand for big data storage.However,problems such as short transmission distance and short node life of traditional communication methods such as bluetooth,Zig Bee and Wi Fi also restrict t he development of the Internet of things.When the LPWAN protocol proposes the Internet of things nodes with low bandwidth,low power consumption,long distance and a large number of connections,Semtech proposed that LoRa technology can make the transmiss ion distance of nodes reach 10 KM and the nodes life reach 10 years,which make the life span and transmission distance of nodes the main factors.This paper mainly studies Lora acquisition system and data compression storage.The node hardware of LoRa acquisition system uses SX1276 as a low-power wireless transceiver,the gateway uses SX1301+Raspberry Pi as the hardware and LoRa WAN protocol for da ta transmission.To study Data collection of LoRa big data flow in this paper,it selects the optimized C lass B mode as the transmission mode and set continuous data collection in a fixed time.In order to make the whole energy saving system adopt the network mechanism of optimized node,and it combines the C lass B pattern with the C lass A pattern to form a new pattern,and the nodes are automatically netted in C lass A mode.After networking,data collection is transmitted to the local server in C lass B mode.The server analyzes the gateway networking data to see if there is a node communicating with multiple gateways.After networking is completed,the data collected by a sensor of the node is compared with the data of the previous moment.At the same time,if the data collected by the nodes are the same as that at the previous moment,then the data transmitted by the nodes will reduce the data volume and loss of the nodes energy consumption.When the node transmits the data to the database,in order to reduce the storage space of the database,this paper combines the BP neural network data compression principle to compress the historical data.Meanwhile,particle swarm optimization(pso)is used to optimize BP neural network compression algorithm,which is a lso compared with the real-time data compression storage revolving door compression algorithm.The PSO-BP algorithm is basically consistent with the original data in data reduction after data compression which ensures the original data while the data compression ratio reaches about 80%.At the same time,the upper computer is designed to display the data collected by the nodes and the node control and successfully run the designed energy saving and data compression storage system of large data flow.This system can meet the design requirements,reduce the energy consumption of nodes and local system space for data storage.