Abstract-In this work, we consider a multiuser cooperative wireless network where the energy-constrained sources have independent information to transmit to a common destination, which is assumed to be externally powered and responsible for transferring energy wirelessly to the sources. The source nodes may cooperate, under either decode-and-forward or network coding-based protocols. Taking into account the fact that the energy harvested by the source nodes is a function of the fading realization of inter-user channels and user-destination channels, we obtain a closed-form approximation for the system outage probability, as well as an approximation for the optimal energy transfer period that minimizes such outage probability. It is also shown that, even though the achievable diversity order is reduced due to wireless energy transfer process, it is very close to the one achieved for a network without energy constraints. Numerical results are also presented to validate the theoretical results.

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

The technological paradigm of the Internet of Things has attracted the attention of the market, industry, and scientific community. The possibility of integrating wireless sensor network (WSN) devices to the Internet has prompted the Internet Engineering Task Force (IETF) to specify new standards and protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL), designed to find stable routing paths via links that have considerable losses. Among the routing metrics, the expected transmission count (ETX) is notable because its implementation in RPL helps choosing reliable paths. However, the rapid exhaustion of battery energy at bottleneck nodes remains a problem. In this context, this study introduces the network interface average power metric (NIAP), a new metric based on the estimated average power consumption of the network interface, which contributes not only to the choice of reliable paths but also to load balancing and lifetime increasing of a wireless sensor network. The results of several experiments conducted in a simulated environment demonstrate that NIAP is a promising alternative to ETX due to its simple implementation without modifications of the RPL standard.

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Motivated by new wireless applications that rely on ultra-reliable low latency communications, while supporting the transmission of short packets, we introduce a method that reduces the wireless resources consumption for real-time control of physical systems. Leveraging the tight interaction between control and communication systems, we make use of packetized predictive control along with incremental redundancy hybrid automatic repeat request, aiming at minimizing the energy consumption of a packet by optimizing the transmit power and prediction length of the controller. Our results show that the proposed strategy can save up to 45% of wireless resources when compared to a state-of-the-art method.