• Improved Kalman filter based differentially private streaming data release in cognitive computing.

      Wang, Jun; Luo, Jing; Liu, Xiaozhu; Li, Yongkai; Liu, Shubo; Zhu, Rongbo; Anjum, Ashiq; University of Derby; South-Central University for Nationalities; Wuhan University of Technology; et al. (Elsevier, 2019-04-04)
      Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
    • Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks

      Liu, Xiaozhu; Zhu, Rongbo; Anjum, Ashiq; Wang, Jun; Zhang, Hao; Ma, Maode; Wuhan University of Technology, Wuhan, China; South-Central University for Nationalities, Wuhan, China; University of Derby; Nanyang Technological University, Singapore (Elsevier, 2019-10-04)
      Data fusion can effectively reduce the amount of data transmission and network energy consumption in wireless sensor networks (WSNs). However the existing data fusion schemes lead to additional delay overhead and power consumptions. In order to improve the performance of WSNs, an intelligent data fusion algorithm based on hybrid delay-aware clustering (HDC) in WSNs is proposed, which combines the advantages of single-layer cluster structure and multi-layer cluster structure, and adaptive selects the clustering patterns of the cluster by the decision function to achieve the tradeoff between network delay and energy consumption. The network model of HDC is presented, and theoretical analysis of the delay and energy consumption of single-layer cluster and multi-layer cluster are provided. And the energy efficient clustering algorithm and the dynamic cluster head re-selection algorithm are proposed to optimize network energy consumption and load balancing of the network. Simulation results show that, compared with the existing delay-aware models, the proposed scheme can effectively reduce the network delay, network energy consumption, and extend the network lifetime simultaneously.