Design And Implementation Of The Data Collection System For Radio Environment Map

With the rapid development of wireless communication technology,radio resources are applied to vary fields and the demand for radio spectrum is increasing.To reveal the radio environment accurately,the Radio Environment Map(REM)is generated.The REM is applied to characterize the radio environment information.Cognitive wireless system can obtain radio information by accessing and querying REM.The REM provides a new radio mean for radio management.The data collected to construct a complete REM including the information of the signal strength of the sensing area,the data acquisition time,the geographical location of the data acquisition,and the channel occupation time and other data.Radio is ubiquitous,so building a REM is also facing a wide range coverage problems.The primary work of building a REM is data collection.However,building a data collection system is the key to data collection.To solve these problems,this thesis analyzed the practical application requirements of the REM data collection system.We also design and implement a system architecture based on Android data collection client,Web client and Hadoop data center.In order to solve the problem of complex data and wide coverage in the construction of REM,this thesis uses Mobile Crowd Sensing(MCS)to collect data,and proposes a user context identification method.This method can reduce the users' terminal equipment power and bandwidth consumption while ensuring the quality of collected data.To meet the real-time requirement of constructing the REM,this thesis designs a radio environment information processing system for automatic transmission and processing of data to improve the real-time performance of the system.The experiments results show that the design and implementation of the REM data collection system in this thesis achieves the goal in terms of system concurrency,stability and data integrity.