Towards scalable Community Networks topologies

Abstract

Community Networks (CNs) are grassroots bottom-up initiatives that build local infrastructures, normally using Wi-Fi technology, to bring broadband networking in areas with inadequate offer of traditional infrastructures such as ADSL, FTTx or wide-band cellular (LTE, 5G). Albeit they normally operate as access networks to the Internet, CNs are ad-hoc networks that evolve based on local requirements and constraints, often including additional local services on top of Internet access. These networks grow in highly decentralized manner that radically deviates from the top-down network planning practiced in commercial mobile networks, depending, on the one hand, on the willingness of people to participate, and, on the other hand, on the feasibility of wireless links connecting the houses of potential participants with each other. In this paper, we present a novel methodology and its implementation into an automated tool, which enables the exercise of (light) centralized control to the dynamic and otherwise spontaneous CN growth process. The goal of the methodology is influencing the choices to connect a new node to the CN so that it can grow with more balance and to a larger size. Input to our methodology are open source resources about the physical terrain of the CN deployment area, such as Open Street Map and very detailed (less than 1 m resolution) LIDAR-based data about buildings layout and height, as well as technical descriptions and pricing data about off-the-shelf networking devices that are made available by manufacturers. Data related to demographics can be easily added to refine the environment description. With these data at hand, the tool can estimate the technical and economic feasibility of adding new nodes to the CN and actively assist new CN users in selecting proper equipment and CN node(s) to connect with to improve the CN scalability. We test our methodology in four different areas representing standard territorial characterization categories: urban, suburban, intermediate, and rural. In all four cases our tool shows that CNs scale to much larger size using the assisted, network-aware methodology when compared with de facto practices. Results also show that the CNs deployed with the assisted methodology are more balanced and have a lower per-node cost for the same per-node guaranteed bandwidth. Moreover, this is achieved with fewer devices per node, which means that the network is cheaper to build and easier to maintain.

On the Properties of Next Generation Wireless Backhaul

“…Since the publication of realistic 𝐺 𝑣 topologies is one of our contributions, we stress its importance showing that the performance of some state-of-the-art proposals for the creation of 𝐺 𝜑 change significantly when applied to a real 𝐺 𝑣 or to one generated with simplistic assumptions. The work that is somehow closer to this paper is our previous contribution [24], which uses a similar ray-tracing approach, but with the objective of comparing growth strategies for Community Networks. The methodology is also different: This work uses a GPU-based approach that enables the computation of the whole 𝐺 𝑣 for entire cities to extend the analysis to an unprecedented scale.…”

A Realistic Open-Data-based Cost Model for Wireless Backhaul Networks in Rural Areas

“…In [6] the authors investigate the economic feasibility of the growth of a wireless community network. In [7] a WISP backhaul optimization model is formulated in order to minimize the energy consumption.…”

“…Outdoors there is one pole on which wireless devices (devices, from now on) are mounted, these are ISP-grade radio devices that create point-to-point or point-to-multipoint links, which we we assume operate in the 5GHz ISM bandwidth with 802.11ac. This configuration has been used in real mesh networks made of hundreds of nodes studied in the literature [6], [16], but our model could be easily modified to use different technologies.…”

WIP: Analysis of Feasible Topologies for Backhaul Mesh Networks

“…The recent introduction of wireless backhaul in the 5G standardization documents (referred to with Integrated Access and Backhaul, IAB) have further increased the interest in this topic [4]- [6], because 5G (and beyond) requires a densification of base stations that can be achieved only with wireless backhauls, especially in rural areas. Out of the cellular network application, mesh networks have been used and are still used to provide connectivity where traditional networks cannot be deployed or are not profitable [7]- [9].…”

“…This paper leverages the analysis of real data extracted by the tri-dimensional building shapes of 9 Italian municipalities, reported in Tab. I: three are Urban, three Suburban, and three More details on the process of creation of the data-set can be found in [9], [10], which we refer to as TrueNets.…”