Wireless sensor networks (WSNs) are technologies that play an important role in Internet of Things (IoT) systems. In most applications, sensor nodes are expected to operate for extended periods without maintenance. Therefore, minimizing power consumption and self-sustainability based on energy harvesting (EH) still represent important research challenges. In this article, a power management strategy (PMS) based on the weighted order statistics (WOS) classification technique is proposed to dynamically adapt the duty cycle of the sensor node according to historical data measurements. Unnecessary acquisition and transmission of slow-varying signals are reduced, improving the power consumption of the node. As traditional renewable sources (sunlight, wind, and vibration) are scarce in indoor scenarios, an array of Dypsis Lutescens plants is used as a power bioenergy cell, providing a clean and cost-effective alternative to power indoor sensor nodes. The WOS technique is programmed into an nRF52840 microcontroller, and an ultralow-power BQ25570 harvesting circuit harnesses the energy from the array of plants. Experimental results include the energy performance analysis of the wireless sensor node and the measured power generation capacity of the bioenergy source, showing an energy-autonomous behavior for IoT applications. Average power consumption of 15.6 mW per transmitted data packet of 14 bytes is achieved, which represents the ability to perform at least 693 data transmissions per day considering an 8 F supercapacitor as a storage device.

For control applications, the angular velocity of the drive crank of a four-bar mechanism is traditionally assumed to be constant. In this paper, we propose control of variable velocity of the drive crank to obtain the desired output motions for the coupler point. To estimate the reference trajectory for the crank velocity, a neural network is trained with data from the kinematic model. The control law is designed from feedback linearization of the tracking error dynamics and a Proportional–Integral–Derivative (PID) controller. The applicability of the proposed scheme is validated through simulations for three variable speed profiles, obtaining excellent results from the system.

Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.

This work proposes a model-based control scheme using a sliding mode controller (SMC) and an immersion and invariant (I&I) observer. The objective of the proposed control scheme is to be applied to a three-phase grid-tied inverter, which could operate as a shunt active power filter when the photovoltaic array is not generating power (night-time operation). The grid-tied inverter always remains operational, ensuring continuous support of the power quality improvement, as well as current harmonic compensation due to nonlinear loads and power factor correction. An external control loop is included to keep the voltage of the DC-link capacitor regulated. As can be explained in detail along with the work, a battery stack is avoided in this proposed research. Thus, a decision stage is added to the control scheme to select the night or day operation. Simulation results are carried out using Altair-PSIM© to demonstrate the effectiveness of the proposed control scheme in several scenarios.

Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on institutional subscriptions for metadata retrieval. Most importantly, they still require manual selection of literature. This paper introduces a framework that automates keyword searching in article abstracts to help select relevant literature for the SOTA by identifying key terms matching that we, hereafter, call source words. A case study in the food and beverage industry is provided to demonstrate the algorithm’s application. In the study, five relevant knowledge areas were defined to guide literature selection. The database from scientific repositories was categorized using six classification rules based on impact factor (IF), Open Access (OA) status, and JCR journal ranking. This classification revealed the knowledge area with the highest presence and highlighted the effectiveness of the selection rules in identifying articles for the SOTA. The approach included a panel of experts who confirmed the algorithm’s effectiveness in identifying source words in high-quality articles. The algorithm’s performance was evaluated using the $F_1$ Score, which reached 0.83 after filtering out non-relevant articles. This result validates the algorithm’s ability to extract significant source words and demonstrates its usefulness in building the SOTA by focusing on the most scientifically impactful articles.