Precision And Privacy-preserving Oriented Data Aggregation Algorithm In Sensor Networks

Wireless Sensor Networks(WSNs) is an important part of Internet of Things(IoT). Providingprivacy-preserving mechanism in data aggregating is an effective way to protect security of data inWSNs. However, privacy-preserving mechanism in other fields is not always suitable for WSNsbecause of its own particularity in WSNs. Therefore, research on security in WSNs is an interestingproject to be explorered.Recently, researches on security in WSNs have reached certain achievements, at home andabroad. Research development can be described in the following way: introduce intrusion detectionto enhance security—ensure data not to be revealed by attackers from outside of network—preventsensitive data from being disclosed to any node in the network, security has been enhancedgradually. Among these schemes, SMART algorithm is a good scheme which not only ensures datanot to be revealed by attackers from outside of network by communication encryption, but alsoguarantee in-network confidentiality by data slicing operation.In this thesis, we details the performance deficiencies in SMART algorithm, and in allusion toits drawback of low aggregation accuracy, we present five optimizing factor. Particularly, theintroduction of partial factor also enhances privacy-preserving efficacy and somehow cuts downcommunication overhead and then the energy consumption, which therefore, prolongs the lifetimeof the network. Theoretical and practical results reveal that all optimizing factors achieve theirdesired results. Moreover, PEPDA algorithm demonstrates better performance in data aggregationprecision, privacy-preserving and communication efficiency compared with SMART.