Journal ArticleDOI
ROSE: Robustness Strategy for Scale-Free Wireless Sensor Networks
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TLDR
ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed, which exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks.Abstract:
Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.read more
Citations
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References
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Mean-field theory for scale-free random networks
TL;DR: A mean-field method is developed to predict the growth dynamics of the individual vertices of the scale-free model, and this is used to calculate analytically the connectivity distribution and the scaling exponents.
Book
Complex Networks: Structure, Robustness and Function
Reuven Cohen,Shlomo Havlin +1 more
TL;DR: This chapter discusses random network models, which are based on the Erdos-Renyi models, and their application in the context of complex networks, where distances in scale-free networks are small and distances in complex networks are large.
Journal ArticleDOI
Mitigation of malicious attacks on networks
TL;DR: It is shown that with small changes in the network structure (low cost) the robustness of diverse networks can be improved dramatically whereas their functionality remains unchanged and is useful not only for improving significantly with low cost the robustity of existing infrastructures but also for designing economically robust network systems.
Journal ArticleDOI
Robustness of interdependent networks under targeted attack.
TL;DR: It is found that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero.
Journal ArticleDOI
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks
TL;DR: It is shown that load balancing is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.