Life Cycle and Intrusion Tolerance Optimization Topology Models for Wireless Sensor Networks
DOI:
https://doi.org/10.3991/ijoe.v14i05.8643Keywords:
Wireless sensor networks, Network topology, Intrusion tolerance, Life cycle, En-ergy, Node failureAbstract
Wireless sensor networks have such disadvantages as upper limit of node energy and poor intrusion tolerance, etc. In light of these disadvantages, by analyzing such key parameters as residual energy, load, node degree, this paper proposes a wireless sensor network (WSN) life-cycle model, which fully considers node energy consumption and load fault tolerance, and a scale-free intrusion tolerance and targeted attacks optimization topology model. Then it verifies their feasibility through simulation test. The results show that the WSN life cycle model takes into account the impacts of residual energy and load capacity on the life cycle and fault tolerance of the system and improves the connectivity probability of high energy consumption nodes and small load nodes, leading to more uniform energy consumption of the wireless sensor network. Through the load adjustment coefficient, the life cycle of the network model is significantly increased. The simulation results show that the fault tolerance and survival time of the proposed model are both improved to some extent compared with those of other models. The proposed scale-free intrusion tolerance and targeted attacks optimization topology model optimizes the power exponent of the network using the structure entropy, and the established scale-free topology structure can make the model more tolerant to intrusion. The simulation results show that the intrusion tolerance of the algorithm proposed in this paper is 2.5 times that of the traditional network model, and the average life cycle is also significantly increased compared to those of other models.
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Published
2018-05-25
How to Cite
Lei, J., Tian, X., & Zhang, Z. (2018). Life Cycle and Intrusion Tolerance Optimization Topology Models for Wireless Sensor Networks. International Journal of Online and Biomedical Engineering (iJOE), 14(05), pp. 105–117. https://doi.org/10.3991/ijoe.v14i05.8643
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