Wireless sensor networks (WSNs) are starting to have a high impact on our societies and, for next generation WSNs to become more integrated with the Internet, researchers recently proposed to embed IPv6 into such very constrained networks. Also, constraint application protocol (CoAP) and Observe have been proposed for RESTful services to be provided. CoAP/Observe supports the use of caches/proxies and, for this reason, an observation request may resort to multiple client/server registration steps in order to get notifications. Here, we propose to plan the multiple registration steps, at proxies, of multiple observation requests in order to make proper aggregation/scheduling of notifications for transmission. This leads to less energy consumption and to an effective use of bandwidth, avoiding energy depletion of nodes, and increasing the network lifetime. Besides, mathematically formalizing the problem, a heuristic approach is developed and a discussion on how to incorporate algorithm's decision into the network is done. The proposed framework can be applied to multiple application domains (e.g., monitoring, machine to machine).

Evapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration ( ET_o ) estimation models, having as input limited meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ET_o , SR estimation models are also developed. These ET_o and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN and GRU-ANN. Two main approaches were taken for ET_o estimation: (i) directly use those algorithms to estimate ET_o , and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ET_o . For the later case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of FAO-56PM method to compute ET_o , which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination ( R ² ) of 0.977. The developed model were then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar ( R ² > 0.95) results to the ones for which the models were initially designed, proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.

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Energy-efficient network design has recently become a very important topic because of the energy cost increases in service providers’ infrastructures. This is of particular importance in access networks because of the growing demand for digital traffic by end users. Here we address the challenge of reducing the energy consumption of fiber–wireless (FiWi) access networks, which use both optical and radio frequency technologies to provide high bandwidth and ubiquity for end-user applications, while keeping delay under a threshold. Our goal is to find optimal sleep mode schedulings that allow energy consumption to be reduced while keeping packet delay acceptable. For this purpose a mathematical formalization and an algorithm are developed. The results show that the proposed approach is able to reduce the average packet delay, with negligible energy cost increases, in many scenarios, besides being computationally efficient and scalable. The proposed approach may, therefore, serve as a basis for planning and design of quality-of-service-aware energy-efficient FiWi access networks.