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2022-04-13
Wang, Chengyan, Li, Yuling, Zhang, Yong.  2021.  Hybrid Data Fast Distribution Algorithm for Wireless Sensor Networks in Visual Internet of Things. 2021 International Conference on Big Data Analysis and Computer Science (BDACS). :166–169.
With the maturity of Internet of things technology, massive data transmission has become the focus of research. In order to solve the problem of low speed of traditional hybrid data fast distribution algorithm for wireless sensor networks, a hybrid data fast distribution algorithm for wireless sensor networks based on visual Internet of things is designed. The logic structure of mixed data input gate in wireless sensor network is designed through the visual Internet of things. The objective function of fast distribution of mixed data in wireless sensor network is proposed. The number of copies of data to be distributed is dynamically calculated and the message deletion strategy is determined. Then the distribution parameters are calibrated, and the fitness ranking is performed according to the distribution quantity to complete the algorithm design. The experimental results show that the distribution rate of the designed algorithm is significantly higher than that of the control group, which can solve the problem of low speed of traditional data fast distribution algorithm.
2018-09-28
Li-Xin, L., Yong-Shan, D., Jia-Yan, W..  2017.  Differential Privacy Data Protection Method Based on Clustering. 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :11–16.

To enhance privacy protection and improve data availability, a differential privacy data protection method ICMD-DP is proposed. Based on insensitive clustering algorithm, ICMD-DP performs differential privacy on the results of ICMD (insensitive clustering method for mixed data). The combination of clustering and differential privacy realizes the differentiation of query sensitivity from single record to group record. At the meanwhile, it reduces the risk of information loss and information disclosure. In addition, to satisfy the requirement of maintaining differential privacy for mixed data, ICMD-DP uses different methods to calculate the distance and centroid of categorical and numerical attributes. Finally, experiments are given to illustrate the availability of the method.