The Safe And Reliable Data Collection Algorithm In WSN

Wireless sensor network(WSN)is the main method of data gathering in networks.With its efficiency and economy,WSN attracted many researchers in recent years.Limited by objec-tive conditions such as the hardware of sensor network,network resources and so on,WSN is usually disturbed by various factors in data gathering,so the safety and accuracy of data will be affected.Existing studies show that,there are various attacks aiming at data in WSN,such as data eavesdropping or destruction.Considering the safety of data,data transmission should be encrypted,but data encryption will increase the computation overhead on sensor nodes,and reduce the efficiency of network communication.Data gathering based on compressed sensing can greatly reduce the communication overhead in networks,so it is suit for data encryption.Meanwhile,the integrity and accuracy of data gathered have attracted more and more atten-tion.Moreover,conventional data recovery can only recover loss data,with abundant corrupted data appear in collected data,detecting and recovering corrupted data become more and more challenging.This paper focus on these two issues with in-depth research,the main work and innovation points are shown as follows:1.In order to protect the safety of data transmission in WSN,this paper proposes a novel Efficient Privacy-Preserving Compressive Data Gathering Scheme which exploits homomor-phic encryption functions in compressive data gathering.With homomorphic encryption on the compressive sensing encoded sensory reading messages,the proposed scheme offers two significant privacy-preserving features,message flow untraceability and message content confi-dentiality,for efficiently thwarting the traffic analysis attacks.The results of experiment demon-strate that proposed scheme can make it efficient to aggregation with the privacy preserved and also make it less computation and communication overhead.2.In order to detect and recover the corrupted data in WSN,this paper proposes a novel approach based on matrix completion(MC)to recover the successive missing and corrupted data.By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou,China,we verify that weather data have the features of low-rank,temporal stability,and spatial correla-tion.Moreover,from simulations on the real weather data,we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way.Mo-tivated by these observations,we propose a novel Principal Component Analysis(PCA)-based scheme to efficiently identify the existence of data corruption.We further propose a two-phase MC-based data recovery scheme,named MC-Two-Phase,which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data ma-trix due to either data missing or corruption.Finally,the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.