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Community networks are becoming increasingly popular due to the growing demand for network connectivity in both rural and urban areas. Community networks are owned and managed at the edge by volunteers. Their irregular topology, the heterogeneity of resources and their unreliable behavior claim for advanced optimization methods to place services in the network. In particular, an efficient service placement method is key for the performance of these systems. This work presents the Multi-Criteria Optimal Placement method, a novel and fast two-stage multi-objective method to place services in decentralized community network edge micro-clouds. A comprehensive set of computational experiments is carried out using real traces of Guifi.net, which is the largest production community network worldwide. According to the results, the proposed method outperforms both the random placement method used currently in Guifi.net and the Bandwidth-aware Service Placement method, which provides the best known solutions in the literature, by a mean gap in bandwidth gain of about 53% and 10%, respectively, while it also reduces the number of resources used. This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities (PGC2018-097599-B-100 and PID2019-106774RB-C21), and by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 (DiPET CHIST-ERA) and PCI2019-111851-2 (LeadingEdge CHIST-ERA).

Universal connectivity is still unavailable or expensive for half of the global population, despite being critical for social participation. The deployment of crowdsourced networking infrastructures creates an opportunity for local development, where anyone can deploy a new device. In such infrastructures connectivity offer can expand incrementally and be sustainable through investment and fees resulting from the demand and consumption of content and services, including Internet access, that compensate the cost of the underlying network. While routing coordinates network data flows, economic flows can be coordinated by smart contracts built over a local distributed ledger. We define crowdsourced networks, the concept, architecture, and implementation using a local Ethereum PoA blockchain with Solidity smart contracts that compensate the data traffic contribution and consumption recorded by a traffic monitoring system, on a wireless mesh network. The prototype software has been validated in a controlled mesh network environment. Functional tests show its ability to account and route economic flows with small resource consumption, and therefore confirms these networks can develop organically by the addition of consumer and provider participants to reach the typical scale of most wireless mesh access networks and deliver networking services that aim to be socially and economically sustainable.

The Guifi.net community network has evolved during the past 15 years into a telecommunications infrastructure that offers Internet access to more than 80.000 people. The monitoring system currently in place for this network is lagging behind the growth of the infrastructure, requiring manual intervention and counting several single points of failure. In this paper we present REDEMON, a resilient decentralized monitoring system, hosted on distributed and interconnected edge devices, for a reliable, eventually-consistent monitoring of the Guifi.net network, leveraging CRDT-based data structures implemented on AntidoteDB. We developed the REDEMON system as a prototype featuring resilience, decentralization and automation, in order to replace the legacy monitoring system. To assess the system, this prototype was deployed on resource-constraint edge nodes in the Guifi.net production network and evaluated under realistic conditions. The decentralized assignment mechanism successfully achieves setting the minimum number of monitoring servers per network device that satisfies the established system requirements. Besides, by concentrating the workload on the minimum required number of servers running at their maximum capacity, the remaining devices can idle away, reducing the consumption footprint of the system. With regard to computing resources, we measure a moderate CPU and RAM usage by the monitoring system on low-capacity devices, while we observe that a considerable network traffic is required for achieving a resilient and consistent data storage layer. This resilient and decentralized architecture could lay the basis for other edge applications in the cloud computing domain that need to coordinate over distributed and consistent shared data. This work was supported by the European H2020 framework programme project LightKone (H2020-732505), by the Spanish State Research Agency (AEI) under contracts PCI2019- 111850-2 and PCI2019-111851-2, and the Catalan government AGAUR SGR 990.

By leveraging resources from the Fed4Fire+ City-Lab testbed, we design the PiGeon edge computing platform that experiments solution that enable ICN based edge services in wireless mesh networks (WMNs). PiGeon combines into a platform several trends in edge computing namely the ICN (Information-Centric Networking), the containerization of services exemplified by Docker, novel service placement algorithms and the increasing availability of energy efficient but still powerful hardware at user premises (Raspberry Pi, mini-PCs, and enhanced home gateways). We underpin the PiGeon platform with Docker container-based service that can be seamlessly delivered, cached and deployed at the network edge. The core of the PiGeon platform is the Decision Engine making a decision on where and when to deploy a service instance to satisfy the service requirements while considering the network status and available hardware resources. We collect network data from a real citywide mesh network such as CityLab FIRE testbed located at the city of Antwerp, Belgium. The collected data is used to feed our service placement heuristic within the PiGeon platform. Through a real deployment in CityLab testbed, we show that our service placement heuristic improves the response time up to 37% for stateful services (Web2.0 service). Apart from improving the QoS for end-users, our results show that ICN plays a key role in improving the service delivery time as well as reducing the traffic consumption in WMNs. The overall effect of ICN in our platform is that most content and service delivery requests can be satisfied very close to the client device, many times just one hop away, decoupling QoS from intra-network traffic and origin server load. This work was supported by the European H2020 framework programme project Fed4Fire+ (732638), by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 and PCI2019-111851-2, and the Catalan government AGAUR SGR 990.

The growing demand for network connectivity has boosted the number of community networks (CNs). CNs are decentralized and self-organized communication networks owned and managed at the edge by volunteers. Due to the heterogeneity of edge node characteristics, high software and hardware diversity, irregular topology and unreliable behavior of the network, the performance of its services varie depending on where they are hosted. These characteristics of CNs and edge platforms running on them require of advanced simulation-optimization methods to place services. In this context, we propose a simheuristic algorithm to address this stochastic problem. The core of this approach relies on a multi-start metaheuristic with a multi-objective optimization method. Our approach combines Monte Carlo simulation and the multi-criteria optimal placement heuristic, The method is tested using real traces of Guifi.net CN, which is considered to be largest CN worldwide. This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities under contract PGC2018-097599-B-100 and by the Spanish State Research Agency (AEI) under contracts PCI2019-111850-2 and PCI2019-111851-2.

Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low cost Internet access with infrastructures built from routers contributed by diverse owners, while blockchain enables transparency and accountability for investments, revenue or other forms of economic compensations from sharing of network traffic, content and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means every participant can invest in resources, and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self-organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city-wide production mesh network and also in laboratory network. We quantify the performance, bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains., Comment: arXiv admin note: substantial text overlap with arXiv:1804.00561