Survivability-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks

The paper proposes the algorithm of survival topology evolution model. The model applies the survival analysis to the research on the topology of networks. On one hand, it takes the survivability of every node into account. Since the state of a node is affected by the node itself, the environment and so on, it's necessary to study the survivability of nodes. In addition, the survival analysis on the nodes can indicate whether the nodes work well in real time. On the other hand, it also takes the deletion of links and nodes determined by the survivability of the nodes into account. Then it reaches the conclusion by mean field method. The result shows that the degree distributions of WSNs are approximately power law as B-A model and that the survivability of the nodes is proportional to the degree distribution of the network consisting of the previous nodes. These are further confirmed by simulation example.

[1]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[2]  S. N. Dorogovtsev,et al.  Evolution of networks with aging of sites , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Guanrong Chen,et al.  Complex networks: small-world, scale-free and beyond , 2003 .

[4]  Han Zhu,et al.  Effect of aging on network structure. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Kamalika Basu Hajra,et al.  Aging in citation networks , 2004, cond-mat/0409017.

[6]  K. Hajra,et al.  Modelling aging characteristics in citation networks , 2005, physics/0508035.

[7]  Paul L. Krapivsky,et al.  Transition from small to large world in growing networks , 2007, ArXiv.

[8]  Axel W. Krings,et al.  Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs) , 2008, MSWiM '08.

[9]  Axel W. Krings,et al.  Multivariate Survival Analysis (II): An Overview of Multi-State Models in Biomedicine and Engineering Reliability , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[10]  Zhanshan Ma,et al.  Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM) , 2008, 2008 IEEE Aerospace Conference.

[11]  Zhanshan Ma,et al.  Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling , 2008, 2008 IEEE Aerospace Conference.

[12]  Axel W. Krings,et al.  An outline of the three-layer survivability analysis architecture for strategic information warfare research , 2009, CSIIRW '09.

[13]  Xianmin Geng,et al.  Degree correlations in citation networks model with aging , 2009 .

[14]  Zhanshan Ma,et al.  A new life system approach to the Prognostic and Health Management (PHM) with survival analysis, dynamic hybrid fault models, evolutionary game theory, and three-layer survivability analysis , 2009, 2009 IEEE Aerospace conference.

[15]  Lixiang Li,et al.  Complex networks-based energy-efficient evolution model for wireless sensor networks , 2009 .

[16]  Z. Duan,et al.  A weighted local-world evolving network model with aging nodes , 2011 .

[17]  Axel W. Krings,et al.  Dynamic Hybrid Fault Modeling and Extended Evolutionary Game Theory for Reliability, Survivability and Fault Tolerance Analyses , 2011, IEEE Transactions on Reliability.

[18]  Xiaojuan Luo,et al.  Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks , 2012 .

[19]  Nan Jiang,et al.  A Local World Evolving Model for Energy-Constrained Wireless Sensor Networks , 2012, Int. J. Distributed Sens. Networks.