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Core-graph links are likely to cover longer distances. Network evolution Guifi. More- over, the deployment of the physical networking infrastructure has been constantly evolving and adapting to the circumstances of the moment.

Some of the decisions made by guifi. Geographic growth Osona rural area, unplanned deployment. Like any communication network, the phys- ical deployment of guifi.

Osona was the first region where guifi. It is an inland county with an extension of roughly more than 1, Km2. In , Osona had a population density of around 0. Figure 10 illustrates the evolution of guifi. Each map contains a snapshot of the network topology at the end of the year.

Nodes and links depicted in light colours represent elements that already existed in the previous snapshot of the network. In contrast, new links that were added during the year under consideration are shown in darker colours. All the elements have been placed according to geographic coordinates. During the first years from in Figure 10a to in Figure 10c , founders focused their efforts on improving the software and hardware technologies behind guifi.

Because wireless technologies by then were not as mature as they are today, any potential community members were computer savvy people with high interest in new tech- nologies. In , guifi. It resulted in a two-phase deployment, marked by the lack of initial knowledge in the first phase and a learning process during the second.

Figure 11 shows the evolution of the deployment of guifi. The network deployment was planned to be completed in two years. During the first year, guifi. This method is easier and cheaper in comparison with the cost and effort required for wired networks. The second year, most efforts were focused on completing a communication ring between cities, which provided an alternative for connectivity in case of a link failure. This deployment plan highlights one of the main advantages of wireless technologies.

Barcelona urban area, unplanned deployment. Figure 12 shows the evolution of the guifi. The city has a total area of Given the number of potential users and the geographical area, Barcelona is considered an independent level 5 zone in the guifi. This fact makes it less interesting for potential users and hence, less attractive to third-party investors — like the city council. However, in Barcelona this was caused by the distribution of different heights of the buildings in the area.

In this section, we described three different models of growth in guifi. As stated pre- viously, the use of one a model was highly dependent on the environmental characteristics of the deployment area urban or rural and the period when it took place.

Planned deploy- ments proved to be a fast way to cover large rural areas, but they required some technical knowledge and skill from the community members and also the cooperation of public entities to fund the initial expenses. Network growth Regardless of the geographical constrains, nodes in a network can be interconnected in many different ways and provide several degrees of redundancy. The network evolution will give us an idea of how close the real network deployment is to the static model builds in Section 5.

Figure 13 shows the total amount of operative elements — nodes and links — in three level 5 zones during the period from to We can observe that nodes and links tend to grow at roughly the same speed in the whole network Figure 13a.

This is caused by the fact that when someone wants to be part of the community, the individual usually extends the network by creating a new node and connecting it to an existing one, as illustrated in Figure We can also observe that the average number of links is slightly smaller than the number of networking devices. This can be explained by a behavioural pattern followed by newcomers, who focus their initial efforts on installing and registering a new node and afterwards trying to connect it to the network.

The core networks in Figure 13b, which only contain nodes with more than one link with the largest network component, show big differences between the growth rate of nodes and links after and — one year after the creation of the guifi. By that time, the network was mature enough and had some experienced users, which also maintained most of the core infrastructure.

Therefore, it started to provide networking access and other services through their supernodes, increasing the number of end-users that were directly connected to the core infrastructure. We can observe in Figure 13 that the guifi. In order to understand such behaviour, we performed a comparison of the link and node growths with the evolution of their degrees and link distances, as shown in Figures 14 and In the former figure, the solid lines represent the average node degree in each zone by the end of the year, while in the latter figure, the solid lines represent the average distance of the radio links in each zone.

Both figures contain two subfigures a and b , which show the differences between the entire base-graph and the core-graph, respectively. The bars in all the figures represent the number of nodes that the zone contains at the end of the same period.

Rural and urban zones show different degree patterns over time regardless of whether or not the topological deployment had been planned. According to the figures, it seems that during the first years the degree distribution of nodes in rural areas e. In contrast, for urban areas e. This is because nodes in urban areas do not need to guarantee redundancy or high connectivity with core nodes Figure 14b.

These needs are covered by different means; in guifi. Besides the obvious difference in scale due to the geographic area covered, what is interesting is how all three sub-networks change their tendencies in building longer or shorter links. Interestingly, the growth model of unplanned networks increases the distance be- tween links only in the last mile. We can generalize this observation and state that the deployment cost — and possibly the maintenance as well — of core networks is less expensive in unplanned networks than in planned ones.

In summary, we have seen three different models of deployment — unplanned and planned deployment in rural areas and unplanned deployment in urban areas. To the best of our knowledge, there is no planned deployment in urban areas in guifi. We think that this is caused by the lack of support by urban public administrations and the lack of interest of many citizens, resulting in more effort from the members of the community. Our conclusions were supported by opinions from several network activists [9].

Availability and reachability of the network nodes One of the aspects that distinguish community networks, such as guifi. As is the case in any social community, the knowledge and involvement of individuals can vary; therefore there are no guarantees of connectivity or quality of service.

Furthermore, the quality and state of the heterogeneous hardware also influences the stability of the links and network performance. In this section, we explore this aspect by analysing the availability and reachability of guifi. This can be used as an indirect metric of the quality of connectivity that new members may expect from the network.

Commonly, users do not tend to deliberately reboot the device unless they have to perform an upgrade, which is not very common. Hence, the number of days reported by the sysUpTime is a relatively good measure of the device availability due to random failures.

We also reported the last time a node had been rebooted — voluntarily or not — which gave us a direct measure of its availability. In comparison with the uptime reported in a similar study on PlanetLab [18], a guifi.

The fact that PlanetLab showed a higher average sysUpTime on its nodes may be because it is an experimental testbed running on much more stable computers and environment. Another significant difference between testbeds like PlanetLab and community projects like guifi. While the former typically uses academic networks to reach the Internet and other participating networks, the wireless com- munity networks tend to use only their own infrastructure to reach other nodes.

In practical terms, this means that guifi. Figure 17 shows, as an example, the reachability of each node on the base and the core-graphs built as a log10 -log10 Empirical Cumulative Distribution Function ECDF. The reachability of a node has been calculated using the average availability of all the devices on its location.

The average availability of a device is the percentage of ping requests that the node replies when requested by the graph-server system. As a result, Figure 17 shows the probability of a node being contacted. Numbers are even worse on the core-graph, which is supposed to be the most stable part of the network.

In general, for the guifi. This may be explained by the higher accessibility and proximity between nodes in such urban networks. Analysis of the network resilience In this section, we discuss the resilience of the network resulting from the topological structure built during the past years.

Such uncertainties would be caused by the fact that different sparse geographic zones are connected among themselves though agreements with ISPs, and we cannot infer that information from our dataset. As a result, the analysis in this section focuses on determining the resilience of guifi.

Multiple definitions of robustness and resilience in the literature depend on the gran- ularity of the study and the assumptions made. We used the robustness coefficient R proposed in [19], which provides an easy method to compare our results and to understand the robustness of a topology at the link level — or, in general, any graph — when facing continuous disconnection of its nodes.

Figure 18 shows the R coefficient for all three core-graphs. It has been calculated as the number of nodes that remain connected to the largest network component when top-ranked nodes are progressively being removed. On an ideal network, continuous disconnections would result in a decrease in the number of nodes connected — one unit each time a node is removed.

That is, the size of the largest component decreases only due to the node that has been recently removed, while all other nodes remain part of the largest single component until they themselves are removed. However, in a real network, the disconnection of one node could, depending on the graph structure, disconnect more nodes from the main component. The coefficient R has been calculated as the ratio of areas, A1 and A2 , between the real and ideal network profile.

In scenarios with a random selection of nodes, the robustness coefficient is a good measure of the average resilience of the topology. Both, best- and worst-case scenarios can also be studied if the nodes are ranked using a structural metric.

By definition, to rank nodes in the best-case scenario, we would have to disconnect, at each step, the edge-nodes of the graph, which will generate a maximum R coefficient of one.

In order to analyse the worst- case scenario, we chose to disconnect nodes in terms of degree, closeness, and betweenness centrality.

Nodes with a high degree centrality have a high number of connections to the rest of the network and therefore are more resilient than other nodes to random failures. Closeness centrality for a connected graph is defined as the inverse of the average distance to all other nodes.

The distance for unweighted graphs is defined as the number of hops, while it is defined as the sum of weights that each link has to traverse for weighted graphs. Closeness centrality is a good measure of how efficient a particular node is in propagating information through the network.

On a weighted graph, the edge weights are taken into account. Nodes with higher betweenness centrality are part of the path used by other nodes to efficiently propagate information in the network, which makes them a critical point of failure.

In our particular case, we computed these measures using an unweighted graph, which represents the connectivity pattern between devices. Weights can be added to the graph to represent traffic measurements e. However, it was not expected in Barcelona. We believe that the simultaneous development of the network in different areas of the city produced such a tree-structure.

Regarding the Osona network, it still has similar robustness when nodes are ranked using the closeness centrality. This network also shows a very poor resilience in frontal sustained attacks to other central nodes. This is caused by the organic growth model described in Section 6. Moreover, we can observe that all networks have a very similar robustness coefficient against sustained attacks when nodes are ranked using closeness centrality.

Implications for network services For most of their members, community networks like guifi. The topological resilience discussion provides a baseline to compare both models, but cannot be used to generalize service resilience, as they may have different quality requirements. Web access, for example, requires users to have access to a DNS server to translate domain names into IP addresses and also a gateway called proxy servers in guifi.

We used a procedure very similar to the one described previously to check the robustness of the web access service in front of nodes failures. We ranked the nodes according to centrality metrics and progressively removed them from the main network component.

Then, we kept track of the number of nodes that lost access to all DNS servers or to all web proxy servers. In Barcelona, web access depends only on a few top nodes; therefore, the removal of a few key proxies will have a very significant impact on the service provision.

This second model appears to be more resilient to coordinated service attacks and demonstrates the advantage of planning the deployment. Related work In this section we present a summary of previous studies on real-world wireless network deployments. An analysis of the RoofNet network is presented in [21, 22]. RoofNet was deployed in an urban environment. The authors reported their findings on the link level characteristics of an The RoofNet study focused on the link level characteristics of the deployment.

In our work, we also studied the link level; however, we focused on topology patterns, and the network under consideration is a much larger network than the one presented in RoofNet. Similarly, another study presented an analysis regarding the DPG network [20], which looks at the link level characteristics of outdoor mesh networks. Nevertheless, such work can only be applied to rural settings.

It is important to note that the study that we present in this paper was performed over a real-world community network, while all the studies mentioned previously were conducted on customised testbeds, explicitly built for experimentation.

There are also some studies on wireless networks with real users instead of only experi- mental networks. For example, the MadMesh network [23] reported a measurement study of such a network deployment and its planning. This deployment is a two-tier architecture and operates in both the 2. In addition, our study was conducted in a community network, while the MadMesh study was performed on a commercial mesh network. Another interesting study is the Google Wi-Fi network presented in [24, 25].

It evaluates different aspects of a metropolitan area mesh network. It estimates the coverage properties of the Google Wi-Fi mesh network [25] and presents the usage characteristics for different user devices in the same Google Wi-Fi network [24].

This study of the Google Wi-Fi network provides a greater understanding of a metropolitan area Wi-Fi mesh. One of the main differences of this work in comparison to our study is that, due to the large size of the network, we were able to analyse its characteristics in a variety of environments and not only in a metropolitan area.

Although we only focus on the guifi. Their study focused on web traffic and the use of proxies to access Internet content in rural areas. In addition, in [33] the authors emphasised the importance of the analysis of web traffic patterns. Consequently, in our work we analysed the logs of several guifi. One further work, a study of mesh networks based on Meraki devices [26], focused on performance at the link level.

They studied the impact of the SNR of a link on the bit rate for that link. They also studied the impact of an opportunistic routing protocol. Likewise, we also studied the link level but focused on topology patterns. These studies focused on the topological properties of the link level and the routing performance. A complementary perspective from social sciences is presented in [34].

It looks at a rural Wi-Fi mesh network and the importance of a bottom-up or participatory design. Al- though the context of the study is rural Africa, the characteristics of the community, the environment, and the scale are completely different.

Nevertheless, it has the same open and participatory nature as guifi. In Table 2, we compared our research with prior related work on real-world Wi-Fi network deployments. As we can observe, our study has some unique features: a the scale of our network is much larger in terms of the number nodes, b the network uses several technologies for links, and c it is open and community focused in nature.

The Guifi community network has also been studied considering the organisational [7] and social participation of its users [3]. Its crowdsourced nature, design principles, and governance as a common-pool resource infrastructure [9] have been studied.

This paper complements previous works on guifi. Conclusions This paper presents a technological analysis of the guifi. It was created by its participants, who pool their resources and efforts, to build and operate a local network infrastructure that is considered a common pool resource.

The community exhibits diverse technological and organisational choices, diverse growth, and maturity under a common community license as well as an engaged social network committed to developing its local common network infrastructure. The analysis introduced in this paper identifies several aspects that affect the design, growth, and resilience of the network and its services. This should help in bootstrapping and developing sustainable, scalable, and effective community networks in other communi- ties.

Regions of the world that are inspired by the guifi. This fact could significantly contribute to the collective mission of enabling every citizen to access and participate in the digital world.

Acknowledgements We greatly thank all the people from the guifi. There are thou- sands, but our special thanks go to a few who have shared not only their network but also their knowledge, wisdom, guidance, time, and smiles. References [1] J. Avonts, B. Braem, C. Blondia, C. Barz, H. Rogge, F. Freitag, L. Navarro, J. Bonicioli, S. Papathanasiou, P. Escrich, R. Kaplan, A.

Neumann, I. Vilata i Balaguer, B. Tatum, M. Vega, R. Meseguer, F. Vega, L. Cerda-Alabern, L. Navarro, R. Meseguer, Topology patterns of a community network: Guifi. Cerda-Alabern, On the topology characterization of guifi. Oliver, J. Zuidweg, M. Baig, R. Roca, L. Navarro, F. Freitag, Guifi. Roca, F. Navarro, guifi. Domingo, M. Van der Wee, S. Verbrugge, M.

Chaudet, D. Dhoutaut, I. Lassous, Performance issues with ieee Meseguer, E. Medina, S. Ochoa, J. Pino, A. Neyem, L. Navarro, D. Castignani, L. Loiseau, N. Montavont, An evaluation of ieee Neumann, P. Albert, Emergence of scaling in random networks, science Colin, Fitting heavy tailed distributions: the poweRlaw package, r package version 0.

Clauset, C. Rohilla Shalizi, M. Verespej, J. Piraveenan, S. Uddin, K. Chebrolu, B. Raman, S. Sen, Long-distance Aguayo, J. Bicket, S. Biswas, G. Judd, R. Morris, Link-level measurements from an Bicket, D.



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