Research On Privacy-preserving Data Aggregation In Wireless Sensor Networks

Wireless Sensor Network is a novel type of Ad Hoc Network targeting at environmental monitoring and object tracking, which consists of multiple sensor nodes deployed in certain sensing areas. Multifunctional sensor modules of micro sensor nodes enable the diverse and complex sensing tasks, and provide network owners with rich and beneficial environment perceiving and data collecting applications. The high redundancy of the sensor data collected by massive and densely deployed sensors can cause the severe network resource waste during their transmission process. By exploiting the spatial correlation between sensor data, data aggregation technology can efficiently aggregate the redundant sensor data at the relay sensors to effectively reduce the size and number of transmitted data. The openness of the wireless transmission medium has raised evergrowing public concerns about information security and data privacy. Therefore, researchers have been dedicating to develop applicable solutions for these concerns.Firstly, this thesis introduces features, application scenarios and research trends of Wireless Sensor Networks. Furthermore, the data aggregating methods and privacyperserving methods of Wireless Sensor Networks are analyzed. Besides, some typical privacy-perserving data aggregation methods are classified and further introduced.Secondly, targeting at the privacy and aggregation of multimedia sensor data, the high computational complexity of existing privacy-preserving data aggregation methods, and the lack of considerations about the sparsity, temporal and spatial correlation between multimedia sensor data, the Distributed Compressed Sensing-based Privacy-preserving Data Aggregation mechanism(DCSPDA) is designed in this thesis. By measuring the original sensor data with Distributed Compressed Sensing technology, and learning and reconstructing the data measurements with Least Squares Support Vector Machine, the sink can determine the privacy-preserving data configuration method and the corresponding data aggregation method, to eventually achieve the privacy-preserving data aggregation and transmission. Simulation results show that the proposed method can notably reduce the communication overhead of the network and provide reliable privacy performance at the cost of the reasonable computational complextiy.Next, targeting at the privacy requirements and energy-efficient collecting of massive sensor data generated by large-scale Wireless Sensor Networks and focusing on the “hot-spot” problem caused by the fast energy consumption of relay sensors, the Scalable Privacy-preserving Big Data Aggregation(ScaPBDA) is proposed in this thesis. The identical cluster composition of Cluster Heads and Cluster Members with different number of auxiliary Cluster Heads is designed in the cluster establishment process. Furthermore, the inter-cluster data re-aggregation is proposed in the multi-hop intercluster data forwarding process to exploit the above-mentioned cluster composition. Simulation results show that the proposed method can efficiently aggregate the big sensor data and protect the privacy of the big sensor data to reduce the network resource consumption.Finally, the conclusion and future research directions are given.