Community networks have emerged under the mottosCommunity networks have emerged under the mottos “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.Peer ReviewedPostprint (author's final draft

Underwater sensor networks represent an important and promising field of research due to the large diversity of underwater ubiquitous applications that can be supported by these networks, e.g., systems that deliver tsunami and oil spill warnings, or monitor submarine ecosystems. Most of these monitoring and warning systems require real-time communication in wide area networks that have a low density of nodes. The underwater communication medium involved in these networks is very harsh and imposes strong restrictions to the communication process. In this scenario, the real-time transmission of information is done mainly using acoustic signals, since the network nodes are not physically close. The features of the communication scenario and the requirements of the communication process represent major challenges for designers of both, communication protocols and monitoring and warning systems. The lack of models to represent these networks is the main stumbling block for the proliferation of underwater ubiquitous systems. This paper presents a real-time communication model for underwater acoustic sensor networks (UW-ASN) that are designed to cover wide areas with a low density of nodes, using any-to-any communication. This model is analytic, considers two solution approaches for scheduling the real-time messages, and provides a time-constraint analysis for the network performance. Using this model, the designers of protocols and underwater ubiquitous systems can quickly prototype and evaluate their solutions in an evolving way, in order to determine the best solution to the problem being addressed. The suitability of the proposal is illustrated with a case study that shows the performance of a UW-ASN under several initial conditions. This is the first analytic model for representing real-time communication in this type of network, and therefore, it opens the door for the development of underwater ubiquitous systems for several application scenarios.

Community networks have emerged under the mottos of “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed and decentralized networks. We demonstrate that this type of prediction achieves about a success probability of about 98% in both the short and long term.

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.

Water crises due to climate change, high population growth and increasing demands from industry and agriculture claim for increasing efficiency and universalizing water resources management strategies and techniques. Water monitoring helps providing necessary evidences for making sound decisions about managing water resources both now and in the future. In this work, a low cost and “do it yourself” communication device is proposed to record water production and energy consumption of electric pumpings from deep boreholes/wells, and to predict the impact of the ongoing and previous pumpings in the evolution of the water level in the aquifer. The proposal incorporates an edge-computing approach for the simulation of the aquifer response in real-time. Computation of results of interest is performed at the sensor, minimizing communication requirements and ensuring almost immediate results. An approximated solution to physically based modeling of aquifer response is computed thanks to the a priori expression of the water level time evolution in a reduced basis. The accuracy is enough to detect deviations from expected behaviour. The energy consumption of the device is very much reduced with respect to that of a full modelling, which can be computed off-line for calibrating reduced model parameters and perform detailed analyses. The device is tested in a real scenario, in a mountain subbasin of the Ebro river in Spain, obtaining a good trade-off between performance, price, and energy consumption.

Community networks are an emergent model with mottos like "a free net for everyone is possible" or "don't buy the network, be the network". Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-to End quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of time-series analysis. We apply this prediction technique in the routing layer of large scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.