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Energy balanced uneven clustering algorithm based on ant colony for wireless sensor network
MIAO Congcong CHEN Qingkui CAO Jianwei ZHANG Gang
Journal of Computer Applications    2013, 33 (12): 3410-3414.  
Abstract688)      PDF (807KB)(484)       Save
In the Wireless Sensor Network (WSN) routing, if the node does not fully consider the path node residual energy and link status of the route, some nodes will be dead early, hence the lifetime of the network will be shorten seriously. To resolve this problem, a uneven clustering routing algorithm for wireless sensor network was proposed based on ant colony optimization algorithm. Firstly, the method clustered nodes using uneven clustering algorithm which considered the node energy. Then considering the node need to transmit data as source node, the sink node as destination node, ant colony optimization algorithm was used to do multipath searching, and the searching process fully considered the factors such as transmission energy consumption, path minimum residual energy, transmission distance and transmission hops, time delay and bandwidth of selected link. Several optimal paths that met the conditions were given to complete the information transmission between source and the destination nodes at last. The experimental results show that the lifetime of WSN can be effectively prolonged while fully considering the path transmission energy consumption, path minimum residual energy and transmission hops.
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Location privacy preserving method by combining user preference and differential privacy model
Liang ZHU, Jinqiao MU, Tengfei CAO, Zengyu CAI, Jianwei ZHANG
Journal of Computer Applications    0, (): 106-111.   DOI: 10.11772/j.issn.1001-9081.2024020214
Abstract27)   HTML1)    PDF (2874KB)(97)       Save

Location-Based Social Network (LBSN) combines social network with geographical locations, providing users with novel personalized experiences. The protection of user location privacy is crucial for the secure operation of LBSN systems. To address the problem of rigid location privacy protection methods leading to low data utility and decreased quality of Location-Based Service (LBS) experiences, a User Preference-based and Differentially Private Location Privacy Protection (UPDP-LPP) method was proposed. Firstly, the set of user stay points was obtained by using a stay point extraction algorithm. Secondly, the types of stay points were labeled by using a feature fusion method. Finally, by dynamically obtaining privacy budget and noise sensitivity through user preferences, Laplace noise was added to the privacy radius to protect sensitive user location information. Experimental results on two public real datasets show that the proposed method improves the data utility of privacy protection by more than 10% compared to TLDP (Trajectory Location Data Protection), DPLPA (Differential Privacy-based Location Privacy protection Algorithm), and LPPM (Location Privacy Protection Mechanism) when the privacy budget is the same. It can be seen that UPDP-LPP not only protects user location privacy, but also enhances data utility.

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