In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer nonlinear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of r transmitters) and/or transmission through s different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. Additionally, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed.

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.

The IEEE 802.3az standard provides a mechanism to build energy efficient Ethernet interfaces via a low power idle mode that they can enter when there is no data to transmit. Several competing algorithms have appeared that make use of this mode to minimize energy consumption with little disruption to the traffic. Two algorithms stand out among those because of their simplicity and performance: frame transmission and burst transmission. Although these algorithms have been shown to be very efficient in simulated scenarios, there is a lack of general analytical models for their behavior. In fact, to this date, the only analyzed traffic patterns have been variants of Poisson traffic. In this paper we provide a general GI/G/1 model for energy consumption and traffic delay for both algorithms. We then develop specializations of the general model for Poisson and deterministic traffic. Finally, we validate the model with the help of both synthetic traffic and real Internet traffic traces.

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.

The discontinuous reception (DRX) mechanism is commonly employed in current LTE networks to improve energy efficiency of user equipment (UE). DRX allows UEs to monitor the physical downlink control channel (PDCCH) discontinuously when there is no downlink traffic for them, thus reducing their energy consumption. However, DRX power savings are achieved at the expense of some increase in packet delay since downlink traffic transmission must be deferred until the UEs resume listening to the PDCCH. In this paper, we present a promising mechanism that reduces energy consumption of UEs using DRX while simultaneously maintaining average packet delay around a desired target. Furthermore, our proposal is able to achieve significant power savings without either increasing signaling overhead or requiring any changes to deployed wireless protocols.