Wireless Community Networks (WCNs) are created and managed by a local community with the goal of sharing Internet connections and offering local services. This paper analyses the data collected on three large WCNs, ranging from 131 to 226 nodes, and used daily by thousands of people. We first analyse the topologies to get insights in the fundamental properties, next we concentrate on two crucial aspects: i) the routing layer, and ii) metrics on the centrality of nodes and the network robustness. All the networks use the Optimized Link State Routing (OLSR) protocol extended with the Expected Transmission Count (ETX) metric. We analyse the quality of the routes and two different techniques to select the Multi-Point Relay (MPR) nodes. The centrality and robustness analysis shows that, in spite of being fully decentralized networks, an adversary that can control a small fraction of carefully chosen nodes can intercept up to 90% of the traffic. The collected data-sets are available as Open Data, so that they can be easily accessed by any interested researcher, and new studies on different topics can be performed. WCNs are just an example of large wireless mesh networks, so our methodology can be applied to any other large mesh network, including commercial ISP networks.

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Fast and efficient recovery from node failure, with minimal disruption of routes and the consequent traffic loss is of the utmost importance for any routing protocol. Link-state protocols, albeit preferred to distance vector ones because of faster convergence, still suffer from a trade-off between control message overhead and performance. This work formalizes the routes' disruption following a node failure as an optimization problem depending on the nodes' centrality in the topology, constrained to a constant signaling overhead. Next, it shows that the solution can be found using Lagrange Multipliers. The solution complexity is low enough to be computed on-line on the network routers, thus obtaining the optimal setting of control message timers that minimize the traffic loss following a node failure. The gain obtained is quantified in power-law synthetic topologies, and it is also tested on real network topologies extending the OLSR protocol to use the modified timers, showing that the inevitable approximations introduced in the analysis do not hamper the very good results achievable through this novel approach. The technique can be applied to any link state protocol, including OSPF, and improves route convergence not only upon failures but on every topology modification. Index Terms-Multi-hop networks; mesh networks; ad-hoc networks; centrality; signalling overhead; failure recovery.

The timely and efficient cooperative distribution of a streamlined content in a communication network is a key feature for many applications and services. One of the unsolved problems is the assignment of transmission rates to nodes given the constraints imposed by the topology, so that all nodes receive the stream with the minimal global use of resources. This paper addresses the problem exploiting the notion of eigenvector centrality. It shows that the problem can be solved efficiently in a distributed way if every node is aware of the full network topology and that in certain cases only local information on the network graph is sufficient.

Wireless communications have received much attention during the last decades due to easy implementation, the possibility of delivering multimedia services to rural communities, and the suitability for public safety and for communicating in emergency situations. In particular, a wireless network designed for an emergency scenario must be capable of monitoring sensitive areas and must enable people to connect immediately after a disaster. This article discusses the main features of a wireless network that aims to interconnect several heterogeneous systems and provide multimedia access to groups of people to better monitor a specific area, to have a fast response in case of a disaster, and to efficiently coordinate all of the forces during the disaster management phase.