Centrality metrics are a key instrument for graph analysis and play a central role in many problems related to networking such as service placement, robustness analysis and network optimization. Betweenness centrality is one of the most popular and well-studied metric. While distributed algorithms to compute this metric exist, they are either approximated or limited to certain topologies (directed acyclic graphs or trees). Exact distributed algorithms for betweenness centrality are computationally complex, because its calculation requires the knowledge of all possible shortest paths within the graph. In this paper we consider load centrality, a metric that usually converges to betweenness, and we present the first distributed and exact algorithm to compute it. We prove its convergence, we estimate its complexity and we show it is directly applicable-with minimal modifications-to any distance-vector routing protocol based on Bellman-Ford. We finally implement it on top of the Babel routing protocol and we show that, exploiting centrality, we can significantly reduce Babel's convergence time upon node failure without increasing signalling overhead. Our contribution is relevant in the realm of wireless distributed networks, but the algorithm can be adopted in any distributed system where it is not possible, or computationally impractical, to reconstruct the whole network graph at each node and compute betweenness centrality with the classical approach based on Dijkstra's algorithm.

The recent explosion of interest in blockchains led to a plethora of proposals for their application, including attempts to decentralize some centralized network functions. At the same time, real "distributed wireless networks" are emerging. Community networks, for instance, are large mesh networks made of hundreds of nodes built by communities primarily to solve digital divide, and they are thriving. The challenges these networks face are not only technological: they deal with creating incentives to participate, with the business model they may adopt, and with their internal governance. Very few models have been proposed to apply blockchains to bottom-up distributed networks: we instead expose how they can solve many problems which so far hindered the diffusion of such networks. Maybe we can push this further: a network is, in essence, a system in which all nodes find a rough consensus on the best paths to connect a node with another. Can we use this consensus method to run a distributed ledger and a cryptocurrency within the network itself, rather than simply applying to networks the effects of a blockchain defined in a separate system? This paper introduces this concept, named "Proof of Networking", and discusses its potential avails.

The term blockchain is used for disparate projects, ranging from cryptocurrencies to applications for the Internet of Things (IoT). The concept of blockchain appears therefore blurred, as the same technology cannot empower applications with extremely different requirements, levels of security and performance. This position paper elaborates on the theory of distributed systems to advance a clear definition of blockchain allowing us to clarify its possible role in the IoT. The definition binds together three elements that, as a whole, delineate those unique features that distinguish the blockchain from other distributed ledger technologies: immutability, transparency and anonymity. We note that immutability-which is imperative for securing blockchains-imposes remarkable resource consumption. Moreover, while transparency demands no confidentiality, anonymity enhances privacy but prevents user identification. As such, we raise the concern that these blockchain features clash with the requirements of most IoT applications where devices are power-constrained, data needs to be kept confidential, and users to be clearly identifiable. We consequently downplay the role of the blockchain for the IoT: this can act as a ledger external to the IoT architecture, invoked as seldom as possible and only to record the aggregate results of myriads of local (IoT) transactions that are most of the time performed off-chain to meet performance and scalability requirements.

Many optimization techniques for networking protocols take advantage of topological information to improve performance. Often, the topological information at the core of these techniques is a centrality metric such as the Betweenness Centrality (BC) index. BC is, in fact, a centrality metric with many well-known successful applications documented in the literature, from resource allocation to routing. To compute BC, however, each node must run a centralized algorithm and needs to have the global topological knowledge; such requirements limit the feasibility of optimization procedures based on BC. To overcome restrictions of this kind, we present a novel distributed algorithm that requires only local information to compute an alternative similar metric, called Load Centrality (LC). We present the new algorithm together with a proof of its convergence and the analysis of its time complexity. The proposed algorithm is general enough to be integrated with any distance vector (DV) routing protocol. In support of this claim, we provide an implementation on top of Babel, a real-world DV protocol. We use this implementation in an emulation framework to show how LC can be exploited to reduce Babel's convergence time upon node failure, without increasing control overhead. As a key step towards the adoption of centrality-based optimization for routing, we study how the algorithm can be incrementally introduced in a network running a DV routing protocol. We show that even when only a small fraction of nodes participate in the protocol, the algorithm accurately ranks nodes according to their centrality.