Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.

The relentless growth of wireless applications and data traffic continues to accentuate the long felt need for decentralized, self-managed, and cooperative network architectures. Enlightened by the power of blockchain technology, we propose a blockchain radio access network (B-RAN) architecture and develop decentralized, secure, and efficient mechanisms to manage network access and authentication among inherently trustless network entities. We further identify promising advanced functions made possible by adopting blockchain for open radio access networks. Our test results demonstrate the benefits of the B-RAN architecture. We also present a number of challenges and future research directions.