This work presents a technological analysis of guifi.net, a free, neutral, and open-access community network. Guifi.net consists of more than 27,000 operational nodes, which makes it the world's largest community network in terms of the number of nodes and coverage area. This paper describes the characteristics of the network, the link level topology, its growth over a decade, and its resilience in terms of availability and reachability of network nodes. Our study is based on open data published by guifi.net regarding its nodes and wireless links, monitoring information, community database, and its web portal. The data includes historical information that covers the lifetime of the network. The scale and diversity of the network requires a separate analysis of the subsets of the entire dataset by area or by separating the core from the leaf nodes. This shows some degree of diversity in local characteristics caused by several demographic, geographic, technological, and network design factors.We focus on the following aspects: technological network diversity, topology characteristics, evolution of the network over time, analysis of robustness, and its effect on networking service availability. In addition, we analyse how the community, the technology used, the geographical region where the network is deployed, and its self-organised structure shape the network properties and determine its strengths and weaknesses.The study demonstrates that the guifi.net community network is diverse in technological choices for hardware, link protocols, and channels and uses a combination of routing protocols yet provides a common private IP network. The graph topology follows a powerlaw distribution for links in regions up to a few thousand Km 2 , limited to the scope of wireless links. Network growth has two aspects: a geographic growth of the network core using long distance links with wireless or fibre, and the local growth in density with leaf low-cost leaf nodes. The resilience of the network derived from the nodes' uptime and the structure of the graph varies across different regions with more fragile leafs than core nodes and diverse degrees of graph resilience to random failures or coordinated attacks, such as natural causes, depending on the network planning, structure, and maturity. The guifi.net community network results from a loosely coupled and decentralised organic growth that exhibits large local differences, diverse growth, and maturity under a common community license and social network.
Abstract. Scalability is a key design challenge that routing protocols for ad hoc networks must properly address to maintain the network performance when the number of nodes increases. We focus on this issue by reducing the amount of control information messages that a link state proactive routing algorithm introduces to the network. Our proposal is based on the observation that a high percentage of those messages is always the same. Therefore, we introduce a new mechanism that can predict the control messages that nodes need for building an accurate map of the network topology so they can avoid resending the same messages. This prediction mechanism, applied to OLSR protocol, could be used to reduce the number of messages transmitted through the network and to save computational processing and energy consumption. Our proposal is independent of the OLSR configuration parameters and it can dynamically self-adapt to network changes.
Community networks have emerged under the mottos of “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed and decentralized networks. We demonstrate that this type of prediction achieves about a success probability of about 98% in both the short and long term.Peer ReviewedPostprint (published version
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