Research On Key Techniques Of Sensing Data Processing In The Internet Of Things

The Internet of things(IoT)is another information technology revolution and industry wave following the computer,Internet and mobile communication.It is becoming the key foundation and important engine of green,intelligent and sustainable development of economic society.The new networked and intelligent production,which is characterized by the integration and innovation of the Internet of things,is shaping the core competence of the future manufacturing industry.And these industry applications,mainly applied to industrial,electricity and other fields,are still pretty much part of the IoT's development.From the perspective of overall development of the IoT in recent years,intelligent information processing is still weak,while industry,electricity and other industrial applications are increasingly more complex with many new characteristics that have been shown in the sensing network size,infrastructure and more aspects of these areas.So the sensing data processing of the IoT has become the research focus for the process of the highly integration of the industry and IoT.This paper analyzed and summarized the characteristics of sensing data and the problems faced by the sensing data processing under the new IoT trend.Taking much deeper research in the acquisition,storage and query of data located on Internet of things,the main contributions of this dissertation are as follows:(1)A data gathering strategy based on frequency-adaptive was proposed.To reduce the collection capacity and the transmission consumption of sensing data in the large-scale Internet of things,this dissertation first divides the large network based on node density;then establishes one-dimensional linear regression model by analyzing the linear relationship of collected data in the time series.According to the changing trend of collected data,the strategy adaptively adjusts the time intervals of data acquisition.Experiments show that this strategy significantly reduces the amount of collected data and energy consumption,and had strong portability.Also,this strategy uses missing data estimation model to fill in missing data to ensure data integrity.(2)A ubiquitous storage method of massively sensing data was proposed.To realize the intelligent at the edge of the IoT,improve the real-time performance of data processing and reduce the load of network transmissions,it needed to make part of mass sensing data stored in the front end of the Internet of things.Therefore,a ubiquitous storage model and a method with the hierarchical expansion mechanism as core were proposed.In this mechanism,the extended hash coding was adopted to dramatically increase storage network element to avoid sudden or frequent events data loss and the multi-threshold levels method was used to distribute data to multiple storage network element to avoid load skew.Experiments show that this method makes full use of the storage resource of storage network element in the IoT,maximally meets the storage requirements for the massive sensing data,and obtains better load balancing of data storage.(3)A data query algorithm based on region for the large-scale sensor network was proposed.At present,the application of Internet of things in industry and power is increasingly complex,which requires real-time planning of the monitoring for key areas,important areas which stress should be laid on,or dangerous areas.However,the current query methods of Internet of things cannot meet the flexible query for arbitrary region.Therefore,a query algorithm for variable region on large-scale sensor network was proposed.This algorithm can query an arbitrary region in the large network based on the variable query window,and communication consumption can be reduced by using mapping array instead of actual physical window when the queries are sent down.Building temporary tree was proposed to solve the problem of data aggregation and forwarding.The experimental results show that the algorithmcanquickly query and return the query results of any query region for large-scale sensing network.This not only improves the real-time performance,but also reduces the cost of network communication significantly and improves the lifetime of the sensor network.(4)An efficient Skyline query method for multi-dimensional sensing data was proposed.To meet the real-time requirement of multiple objectives decision-making in the industry applications,e.g.intelligent industry and smart grid,this dissertation proposed an efficient Skyline query method for multi-dimensional sensing data.Instead of filters were carried when the queries were sent down,dynamic filter tuples were gradually built with a few calculations when the query results were collected,so that determining the dominance relation between the nodes to filter out any node not relevant to the query and its sensing data sets.Thus,little non-spatial data was transmitted over the network and the transmission distance was shorter.Then the method used local-cutting tuples to filter out the sensing data inside the node to further control non-spatial data to be uploaded.Experimental results show that this method can quickly return the contour data of monitored area with lower transmission consumption and show good scalability.(5)The above mentioned theoretical results were applied to an engineering project of Paishanlou gold mine of Liaoning Province.Safety Production System in mine based on structure of IoT was established.Monitoring and controlling subsystem based on frequency-adaptive gathering model was developed.The Safety Production System achieved ubiquitous storage of massive sensing data,query based on region as well as multiple objectives decision-making.Since personnel position system has a poor positioning accuracy in mine,adynamic target tracking and predicting algorithm based on sensing data was proposed.Most of the existing dynamic target location technology is only suitable for indoor two dimensions.In the complex scene of electromagnetic interference and obstacles existed in the industrial Internet of things and other areas,they showed relatively low positioning accuracy of the dynamic target,and also cannot predict the location.So a dynamic target tracking and predicting algorithm based on sensing data was proposed.The algorithm established the forecast model based on sensing data.With simulating the motion characteristics of dynamic target by motion equation,Kalman filter was used to dynamical approach the true value from the estimated value and the observed value to predict the position of the target in the next moment.This algorithm has achieved higher location accuracy in the practical application,and realized the prediction of the position of the personnel and the early warning of dangerous areas.It can also successfully analyze the distribution of obstacles under complex construction environment.