Yatindra Nath Singh

Sateeshkrishna Dhuli, Chakravarthy Gopi, Yatindra Nath Singh

Studying network robustness for wireless sensor networks(WSNs) is an exciting topic of research as sensor nodes often fail due to hardware degradation, resource constraints, and environmental changes. The application of spectral graph theory to networked systems has generated several important results. However, previous research has often failed to consider the network parameters, which is crucial to study the real network applications. Network criticality is one of the effective metrics to quantify the network robustness against such failures and attacks. In this work, we derive the exact formulas of network criticality for WSNs using r-nearest neighbor networks and we show the effect of nearest neighbors and network dimension on robustness using analytical and numerical evaluations. Furthermore, we also show how symmetric and static approximations can wrongly designate the network robustness when implemented to WSNs.

Sateesh Kumar Awasthi, Yatindra Nath Singh

To mitigate the attacks by malicious peers and to motivate the peers to share the resources in peer-to-peer networks, several reputation systems have been proposed in the past. In most of them, the peers evaluate other peers based on their past interactions and then aggregate this information in the whole network. However such an aggregation process requires approximations in order to converge at some global consensus. It may not be the true reflection of past behavior of the peers. Moreover such type of aggregation gives only the relative ranking of peers without any absolute evaluation of their past. This is more significant when all the peers responding to a query, are malicious. In such a situation, we can only know that who is better among them without knowing their rank in the whole network. In this paper, we are proposing a new algorithm which accounts for the past behavior of the peers and will estimate the absolute value of the trust of peers. Consequently, we can suitably identify them as a good peers or malicious peers. Our algorithm converges at some global consensus much faster by choosing suitable parameters. Because of its absolute nature it will equally load all the peers in network. It will also reduce the inauthentic download in the network which was not possible in existing algorithms.