The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

In the next few years, Low Earth Orbit (LEO) constellations will become key enablers for the deployment of global Internet of Things (IoT) services. Due to their proximity to Earth, LEO satellites can directly communicate with ground nodes and, thus, serve as mobile gateways for IoT devices deployed in remote areas lacking terrestrial infrastructure. Within this Direct-to-Satellite IoT (DtS-IoT) context, LoRa (Long Range) technology, capable of providing long range connectivity to power-constrained devices, has received great attention. However, serious scalability issues have been observed in LoRa-based DtS-IoT networks when a high number of LoRa devices perform uplink transmissions driven by the straightforward Aloha protocol during the short visibility periods of the passing-by satellites. In this paper, we evaluate some Aloha-based protocols suitable for this kind of networks and present a new adaptive variant that allows LoRa devices to dynamically adjust their uplink transmission rates in order to make the network work near its optimal operating point. Simulation results show that our proposal is able to significantly improve the network throughput in overloaded scenarios without the need for coordination among LoRa devices, listening to the channel nor gateway support.

Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.

Several recent traffic measurement studies have convincingly shown the presence of self-similarity in modern high-speed networks, involving a very important revolution in the stochastic modeling of traffic. Thus the use of self-similar processes has opened new problems and research fields in network performance analysis, mainly in simulation studies, where the efficient synthetic generation of sample paths (traces) corresponding to self-similar traffic is one of the main topics. In this article, we justify the selection of interarrival time instead of counting processes for modeling arrivals. Also, we discuss the advantages and drawbacks of the most important self-similar processes when applied to traffic modeling in simulation studies, proposing the use of models based in F-ARIMA, mainly due to their flexibility to capture both long- and short-range correlations. However, F-ARIMA processes have been little used in simulation studies, mainly because the synthetic generation methods available in the literature are very inefficient compared with those for FGN. In order to solve this problem, we propose a new method that can generate high-quality traces corresponding to a F-ARIMA(p, d, q) process. A comparison with existing methods shows that the new method is significantly more efficient, and even slightly better than the best method for FGN.

In this study, as the last step of a longitudinal study of the impact of social learning and gamification methodologies on learning results in higher education, we have recorded the activity in a software platform based on Moodle, specially built for encouraging online participation of the students to design, carry out and evaluate a set of learning tasks and games, during two consecutive editions of an undergraduate course. Our aim is to confirm the relationships between the patterns of accomplishment of the gamified activities and the network structure of the social graphs associated with the online forums with knowledge acquisition and final outcomes. For this purpose, we have offered two learning paths, traditional and novel, to our students. We have identified course variables that quantitatively explain the improvements reported when using the innovative methodologies integrated into the course design, and we have applied techniques from the social network analysis (SNA) and the machine learning/deep learning (ML/DL) domains to conduct success/failure classification methods finding that, generally, very good results are obtained when an ensemble approach is used, that is, when we blend the predictions made by different classifiers. The proposed methodology can be used over reduced datasets and variable time windows for having early estimates that allow pedagogical interventions. Finally, we have applied other statistical tests to our datasets, that confirm the influence of the learning path on learning results.