Rural areas often face significant challenges in accessing reliable broadband Internet due to high infrastructure costs and low population density. To address this issue, we propose a model for evaluating the performance and the cost of a mesh-based, last-and middle-mile replacement for broadband connection in these underserved regions. We use open data from ten underserved municipalities to assess the demand, plan the mesh network, and estimate the allocated capacity per user. We consider two designs: a low-cost network using the classical 5 unlicensed band, and a high-performance one using mmWave frequency. For both designs, we estimate the Operating Expenditure and the amortized Capital Expenditure using realistic device prices and operating cost estimations. We compare the price of the mesh-based solution with alternatives based on xDSL and satellite connectivity and show that it has competitive prices compared to existing offers, covering a larger portion of households than DSL. We open-source both the code and the elaborated data to reproduce, extend, and improve our results in different settings.

The advent of Wi-Fi in the 1990s stimulated the creation of Community networks (CNs), a new networking paradigm deployed and maintained by their users. The non-profit and grassroots self-organization of CNs has shown significant potential in enhancing communication technologies grounded in ethical values, transcending mere Internet expansion to contribute to broader social impacts. Since its conception, hundreds of CNs have been deployed worldwide. However, many have faltered, primarily due to a lack of solid economic models and legal setup. This paper presents Expansió Xarxa Oberta (eXO), a commons Internet Service Provider (ISP). In contrast to commercial ISPs, eXO follows the same grassroots principles of CNs. Establishing a commons-based ISP is challenging, yet it is a key component for the sustainability and success of CNs. Therefore, our paper aims to dissect the complexity of a commons ISP by explaining eXO, a commons ISP that has already been in production for more than 14 years. To the best of our knowledge, this is the first time that a commons ISP is described in all its components. To describe eXO, we have proceeded in two parts. In the first one, we delineate the organizational structure, economics, funding mechanisms, activities, legal framework, and infrastructure. To have more insight on eXO, in the second part, we conduct a technical analysis of traffic data collected from eXO’s core router, elucidating subscriber usage patterns. Our study encompasses various subscriber categories, including regular and financially subsidized users, diverse access technologies such as Wi-Fi and optical fiber, and traffic characteristics such as volume, TCP/UDP usage, services accessed, and traffic distribution across countries.

Machine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, the main reason being the lack of datasets. This is motivated by the reluctance of network operators to share data about their infrastructure and network failures. In this paper, we attempt to fill this gap using anomaly detection techniques to discern hardware failure events in wireless community networks. For this purpose we use 4 unsupervised machine learning, ML, approaches based on different principles. We have built a dataset from a production wireless community network, gathering traffic and non-traffic features, e.g. CPU and memory. For the numerical analysis we investigated the ability of the different ML approaches to detect an unprovoked gateway failure that occurred during data collection. Our numerical results show that all the tested approaches improve to detect the gateway failure when non-traffic features are also considered. We see that, when properly tuned, all ML methods are effective to detect the failure. Nonetheless, using decision boundaries and other analysis techniques we observe significant different behavior among the ML methods.

Broadband Internet provision is an increasing demand in many rural areas and wireless internet service providers have emerged as an opportunity to fill this need. However, this type of operator typically consists of a small business with little resources, and difficulty to plan and assess a reliable and economically sustainable infrastructure. In this paper, we try to bring some aid to this challenging problem by describing a reliable mesh-based backhaul design, together with a detailed CapEx/OpEx economic assessment. We apply our model using real data from ten Italian rural municipalities. Our numerical results show that having clusters of 200 subscribers, a reliable backhaul could be deployed with a monthly subscription and price per Mb/s extremely competitive compared to existing market offers.

How costs are distributed among the participants is a key question in the management and viability of shared resources . Although all cost-sharing mechanisms are subjective and thus it is eventually up to the participants to accept one or another, some general criteria seem desirable, such as being budget-balanced and that, in any case, a participant pays more when not cooperating with anyone else. In this paper, we analyse the cost-sharing mechanism that the Guifi.net community network has developed and put in practice to split the transit costs among their more than 20 participants for almost a decade. Our results show that the Guifi.net’s cost-sharing mechanism of the external connectivity, which comprises an equal membership fee for each participant plus a proportional distribution of the remaining costs according to the resource consumption, yields a cost assignment similar to the Shapley value . Our analysis also shows that any alternative to the coalition of all participants entails significant total cost increases and detrimental widespread cost allocation .

QUIC is a new transport protocol under standardization since 2016. Initially developed by Google as an experiment, the protocol is already deployed in large-scale, thanks to its support in Chromium and Google's servers. In this paper we experimentally analyze the performance of QUIC in WiFi networks. We perform experiments using both a controlled WiFi testbed and a production WiFi mesh network. In particular, we study how QUIC interplays with MAC layer features such as IEEE 802.11 frame aggregation. We show that the current implementation of QUIC in Chromium achieves sub-optimal throughput in wireless networks. Indeed, burstiness in modern WiFi standards may improve network performance, and we show that a Bursty QUIC (BQUIC), i.e., a customized version of QUIC that is targeted to increase its burstiness, can achieve better performance in WiFi. BQUIC outperforms the current version of QUIC in WiFi, with throughput gains ranging between 20% to 30%.

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment.

Edge computing is shaping the way to run services in the Internet by allowing the computations to become available close to the user's proximity. Many implementations have been recently proposed to facilitate the service delivery in data centers and distributed networks. However, we argue that those implementations cannot fully support the operations in Community Mesh Networks (CMNs) since the network connection is highly intermittent and unreliable. In this paper, we propose PiCasso, a novel multi-access edge computing platform that combines the advances in lightweight virtualisation and Information-Centric Networking (ICN). PiCasso utilises in-network caching and name based routing of ICN to optimise the forwarding path of service delivery. We analyse the data collected from Guifi.net, the biggest CMN worldwide, to develop smart heuristic for the service deployment. Through a real deployment in Guifi.net, we show that our service deployment heuristic HANET (HArdware and NETwork Resources), improves the response time up to 53% and 28.7% for stateless and stateful services respectively. Finally, using PiCasso for the service delivery in Guifi.net, we achieve 43% traffic reduction compared to the traditional host-centric communication.

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of a "careful" placement of micro-clouds and services over the network. To achieve this, this paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is vital to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 211% in terms of bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the loss packet rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic.

Seamless computing and service sharing in community networks (CNs) have gained momentum due to the emerging technology of community network micro-clouds (CNMCs). However, deploying and running services in CNMCs confront enormous challenges to cope with, such as the dynamic nature of micro-clouds, limited capacity of nodes and links, asymmetric quality of wireless links, geographic singularity based deployment model rather than network QoS based, etc. CNMCs have been increasingly used by network-intensive services which exchange significant amounts of data between nodes, therefore their performance heavily relies on the available bandwidth resource in a network. This paper proposes a novel bandwidth-aware service placement algorithm which aims to replace the current random placement adopted by Guifi.net. Our experimental results show that the proposed BASP algorithm consistently outperforms the random placement in Guifi.net by 35% regarding its bandwidth gain. More promisingly, as the number of services increases, the gain tends to increase accordingly.

Summary Community networks are self‐organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user‐provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end‐user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least‐authority file system (Tahoe‐LAFS), a decentralized storage system with provider‐independent security that guarantees privacy to the users. We evaluate how the Tahoe‐LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe‐LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe‐LAFS. We observed that the write operation of Tahoe‐LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe‐LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe‐LAFS can run on community network clouds with suitable performance for the needed end‐user experience. Copyright © 2015 John Wiley & Sons, Ltd.

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 power-law distribution for links in regions up to a few thousand k m 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.

Nowadays there are inexpensive WiFi devices that have fostered the deployment of wireless communities. Well known routing protocols used in the Internet do not fit well to time varying characteristic of wireless networks. This has motivated an intensive research on routing protocols for wireless mesh networks. At this time there are a number of mature and stable implementations that are being deployed in production networks. In this paper we focus on the experimental evaluation of a production Wireless Mesh network being deployed in a testbed at Universitat Politècnica de Catalunya (UPC) and a quarter of the city of Barcelona, Spain. To our best knowledge, this is the first paper where a production community wireless network using 802.11an is analyzed.

Opportunistic Routing (OR) has been proposed to improve the performance of multihop wireless networks by exploiting its broadcast nature. In contrast to traditional routing, where traffic is sent along pre-determined paths, in OR an ordered set of candidates is selected as the next-hop potential forwarders. The candidate selection is a key issue in OR and many algorithms have been proposed in the literature. In this paper, we define a new metric for candidate selection based on the expected distance progress of sending a packet under OR. By using this metric we propose a hop-by-hop candidate selection and prioritization algorithm that we call Distance Progress Based Opportunistic Routing, DPOR. In contrast to other algorithms, in DPOR each node selects its candidates set independently, without considering the other nodes' candidates sets. DPOR only relies on the neighbors' geographic position of forwarder and the links delivery probability between forwarder and its neighbors. We compare our proposal with four other relevant candidate selection algorithms. Our results show that DPOR performance is almost the same as optimum candidate selection algorithm, while DPOR requires less information and run much faster.

In this paper it is analyzed the topology of Guifi.net Wireless Community Network (WCN). WCN have emerged in recent years organized and deployed by the cooperation of its own customers. WCN keep parallelisms with the construction of the Internet, and seems logical to look for common characteristics among them. More specifically, it is investigated whether there are topology patterns of Guifi.net, and to what extend they share the power-law properties that have been found to obey many topology parameters of the Internet. The paper also proposes a topology generator algorithm that it is able to reproduce specific graph properties that have been obtained for Guifi.net.

This paper presents a measurement study of the topology and its effect on usage of Guifi.net, a large-scale community network. It focuses on the main issues faced by community network and lessons to consider for its future growth in order to preserve its scalability, stability and openness. The results show the network topology as an a typical high density Scale-Free network with critical points of failure and poor gateway selection or placement. In addition we have found paths with a large number of hops i.e. large diameter of the graph, and specifically long paths between leaf nodes and web proxies. The usage analysis using a widespread web proxy service confirms that these topological properties have an impact on the user experience.

Channel assignment has been extensively researched for multi-radio wireless mesh networks, but it is still very challenging when it comes to its implementation. In this paper we propose a semi-dynamic and distributed channel assignment mechanism called SICA that uses game theory. To the best of our knowledge this is the first game formulation of channel assignment which takes the co-channel interference into account. SICA is an interference aware, distributed channel assignment which preserves the network connectivity without relying on a common channel nor central node for coordination between mesh routers. SICA applies an on-line learner algorithm which assumes that nodes do not have perfect information. We have simulated SICA and compared against another interference-aware channel assignment mechanism proposed in the literature called Urban-X. Simulation results show that SICA outperforms Urban-X, even using fewer radio interfaces per node.

In recent years there has been a growing interest in Opportunistic Routing as a way to increase the capacity of wireless networks by exploiting its broadcast nature. By contrast to traditional uni-path routing, in opportunistic routing the nodes overhearing neighbor's transmissions can become candidates to forward the packets towards the destination. In this paper we address the question: What is the maximum performance that can be obtained using opportunistic routing? To answer this question we use an analytical model that allows to compute the optimal position of the nodes, such that the progress towards the destination is maximized. We use this model to compute bounds to the minimum expected number of transmissions that can be achieved in a network using opportunistic routing.

We address the design of high-speed packet buffers for Internet routers. We use a general DRAM/SRAM architecture for which previous proposals can be seen as particular cases. For this architecture, large SRAMs are needed to sustain high line rates and a large number of interfaces. A novel algorithm for DRAM bank allocation is presented that reduces the SRAM size requirements of previously proposed schemes by almost an order of magnitude, without having memory fragmentation problems. A technological evaluation shows that our design can support thousands of queues for line rates up to 160 Gbps.

In order to support the enormous growth of the Internet, innovative research in every router subsystem is needed. We focus our attention on packet buffer design for routers supporting high-speed line rates. More specifically, we address the design of packet buffers using virtual output queuing (VOQ), which are used in most modern router architectures. The design is based on a previously proposed scheme that uses a combination of SRAM and DRAM modules. We propose a storage scheme that achieves a conflict-free memory bank organization. This leads to a reduction of the granularity of DRAM accesses, resulting in a decrease of storage capacity needed by the SRAM. In the DRAM/SRAM scheme, SRAM memory bandwidth needs to fit the line rate. Since memory bandwidth is limited by its size, searching for memory schemes having a small SRAM size arises as an essential issue for high speed line rates (e.g. OC768, 40 Gbps and OC3072, 160 Gbps).

In this paper, we evaluate a low latency handoff protocol for MIPv4, the post-registration handoff method. This mechanism proposed by the IETF tries to improve the performance of hierarchical mobile IP by decreasing the handoff latency. We give a detailed description of the protocol behavior by means of an ns simulation and propose a simple queuing model to study the influence of various parameters on the protocol performance.

In this paper, we address the design of a future high-speed router that supports line rates as high as OC-3072 (160 Gb/s), around one hundred ports and several service classes. Building such a high-speed router would raise many technological problems, one of them being the packet buffer design, mainly because in router design it is important to provide worst-case bandwidth guarantees and not just average-case optimizations. A previous packet buffer design provides worst-case bandwidth guarantees by using a hybrid SRAM/DRAM approach. Next-generation routers need to support hundreds of interfaces (i.e., ports and service classes). Unfortunately, high bandwidth for hundreds of interfaces requires the previous design to use large SRAMs which become a bandwidth bottleneck. The key observation we make is that the SRAM size is proportional to the DRAM access time but we can reduce the effective DRAM access time by overlapping multiple accesses to different banks, allowing us to reduce the SRAM size. The key challenge is that to keep the worst-case bandwidth guarantees, we need to guarantee that there are no bank conflicts while the accesses are in flight. We guarantee bank conflicts by reordering the DRAM requests using a modern issue-queue-like mechanism. Because our design may lead to fragmentation of memory across packet buffer queues, we propose to share the DRAM space among multiple queues by renaming the queue slots. To the best of our knowledge, the design proposed in this paper is the fastest buffer design using commodity DRAM to be published to date.