Node centrality awareness via swarming effects

Centralization is a weakness in large scale dynamic topologies and, thus, collaboratively electing at runtime the most impactful (central) nodes is necessary to ensure reliability. However, little has been achieved in measuring the centrality of nodes in an accurate, fast, decentralized and with low overhead method. This paper proposes a swarm-inspired approach (DANIS) to detect the nodes that would most impact the network connectivity if removed. The idea lies on the trivial fact that the more accessible a node is, the more resources per time unit it loses. Experiments on random, scale-free and small-world graph topologies indicate that DANIS achieves higher accuracy, faster convergence and fewer communication overhead compared to other methods.