# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 1 Introduction  
Community Networks (CNs) are large-scale, distributed, and decentralized networking infrastructures composed of several nodes, links, and services, where the resources are made available to a group of people living in the same area. Sometimes, these networks are referred to as \"the Internet by the People for the People\" [1], and offer a solution to the critical problem of closing the digital divide in the developing world [2]. CNs [3] are structured to be open, free, and to respect network neutrality. Such networks rely on the active participation of local communities in the design, development, deployment, and management of shared infrastructure as a common resource, owned by the community, and operated in a democratic fashion. Community networks can be operationalized, entirely or partly, through individuals and local stakeholders, NGOs, private sector entities, and/or public administrations. This kind of networks are extremely diverse and dynamic, because they are composed of decentralized nodes and they mix wired and wireless technologies, several routing schemes, and diverse services and applications [4]. In addition, they grow dynamically with regards to the number of links, their capacity, and services provided. Some relevant examples of CNs include guifi.net [5], FunkFeuer [6], or AWMN [7]. Among many others, the guifi.net CN exemplifies how communities can develop their own network infrastructures as a commons [8], using several interconnected Wireless Mesh Networks. This results in a large-scale and heterogeneous network which uses diverse routing protocols in several network zones. Community Networks characteristics (large, heterogeneous, dynamic, and decentralized) raise multiple challenges of interest for researchers [9]. One of the most important challenges is the effect of the asymmetrical features and unreliability of wireless communications on network performance and routing protocols. Many metric-based routing protocols for wireless mesh networks that track Link Quality (LQ) and select higher-quality links have been proposed to minimize traffic congestion and maximize delivery rate [10; 11; 12; 13]. Hence, when routing packets through an unreliable network, LQ tracking is definitely a key method to apply. Moreover, routing algorithms should avoid weak links as soon as possible [14], and whenever possible [15]. LQ estimation [16] (or prediction [17]) approach increases the improvements achieved by LQ tracking in routing performance. Usually, real-time metrics do not provide enough information to detect the degradation or activation of a link at the right time. Therefore, prediction techniques are needed to foresee LQ changes and take the appropriate measures. _Path Awareness_[18] is a new Internet architectural-property proposal. The evolution of Internet allows different paths with diverse properties. The endpoints must be aware of these path properties (provided by the network), to make informed decisions about the paths to use. Prediction of path changes can improve local node routing decisions, since it can provide the node with an estimation about the future local and remote events. End-to-End Quality (EtEQ) or Path Quality extends the Link Quality concept to the full communication path (between sender and receiver) and it is computed based on the quality of the individual links that conform the communication path. In [19], we analyzed if our previous work on LQ [20] is applicable to the full communication path (EtEQ tracking and prediction) and we determined what differences exist between individual LQ and EtEQ. To the best of our knowledge, no previous work has explored EtEQ prediction in the routing layer of large-scale, distributed, and decentralized systems. In this paper, we extend our previous work [19] by analyzing the Path and Link behavior and determining the computational cost of the EtEQ prediction.