Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

Abstract

Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free “do it yourself” Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.

Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History

“…Recently, time-series analysis has been considered to estimate link quality (LQ) and end-to-end quality (EtEQ) in the routing layer, involving real-world wireless mesh community networks. For instance, Millan et al [19][20][21][22] shown that time-series analysis can be used to improve the performance of the routing protocol, by providing information that allows making appropriate and timely decisions. This contributes to maximize the message delivery rate and minimize traffic congestion at both levels (i.e., LQ and EtEQ) with a small average mean absolute error.…”

“…However, the use of predictors increases the complexity of the routing protocols, because of the additional hardware and software required to make and validate predictions. Moreover, penalty mechanisms are usually introduced to the system when there is a high rate of mispredictions, which negatively affect the performance of these protocols.Prediction mechanisms have been embedded in routing protocols to foresee several aspects of a network, such as nodes mobility [13], reliability of its topology [14,15] and quality of links and end-to-end paths [16][17][18][19][20][21][22][23][24]. These mechanisms have also been used to reach particular communication…”

Refined Network Topology for Improved Reliability and Enhanced Dijkstra Algorithm for Optimal Path Selection during Link Failures in Cluster Microgrids

“…End-to-end quality of wireless community networks [16] Individuals create community networks to provide free Internet access. End-to-end quality (EtEQ) tracking is used by link-state routing systems to pick pathways that maximize delivery rate while minimizing traffic congestion.…”