LPWAN technologies are playing a major role in the IoT scenario due to their extensive radio range, use of free frequency bands, and low energy consumption. Among these technologies, longrange WAN (LoRaWAN) transmits through several kilometers with data rates up to 5.5 kb/s, making it useful for applications such as smart city monitoring, precision agriculture, and environmental tracking. However, LoRaWAN supports only one-hop communication between end nodes and gateways. Because links can be kilometers long, LoRaWAN signals may encounter obstacles or interference, affecting packet reception. Therefore, different studies have proposed using routing schemes to create multihop communication and improve LoRaWAN performance. The article has the following contributions: first, we present the main approaches to build multihop communication in LoRa networks. Second, we summarize different routing protocols proposed for the technology. Finally, we describe different challenges to improve the performance of routing protocols for LoRaWAN.

There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

Low-Power Wide-Area Network technologies allow sending sensor information through long distances with low data rates. LoRa is one of these technologies, which is less susceptible to noise and interference. This technology operates in unlicensed bands, increasing the chances of having interfering devices in the communication link. The issue of LoRa jamming with external interference and the effect of such interference in radio range has not been addressed in the literature. Therefore, tests measured packet loss, received signal strength, and signal to noise ratio with one end node located kilometers away from a gateway. Data was measured with and without external interference, to establish system robustness. The paper computes an empirical model of the Immunity Region, where the level of interference does not affect LoRa communication. Results show that LoRa devices required interference levels up to 14 dBm to have total packet losses. These results are relevant for implementing LoRa systems, motivating researches to perform site surveys of interference and to account for range reduction in network design.

Microgrids have been proposed as a solution to the growing deterioration of traditional electrical power systems and the energy transition towards renewable sources. One of the most important aspects of the efficient operation of a microgrid is its topology, that is, how the components are connected. Some papers have studied microgrid topologies; however, these studies do not perform an exhaustive analysis of the types of topologies, their applications, characteristics, or technical advantages and disadvantages. The contribution of this paper is the integration of the most important functional properties of microgrid topologies in terms of reliability, efficiency, structure, costs, and control methods. The study analyzes 21 topologies divided into six classifications with their respective sub-classifications. The analysis was based on the characteristics of the current (AC or DC), the control mechanisms, the transition between the operating modes, and the operating costs. As a result of the evaluation, it was evidenced that SST-based completely isolated coupled AC topologies, completely isolated two-stage AC decoupled, and multiple microgrids show the best performances. In contrast, the use of two-stage and three-stage partially isolated AC decoupled topologies is not recommended because of their high operating cost and low efficiency and reliability.

Long-range wide-area network (LoRaWAN) technology can be a good candidate for industrial applications in the Internet of Things and adaptive data rate (ADR) algorithms help to improve LoRaWAN performance. These algorithms compute a link budget to define the data rate and the transmission power. However, the performance of these algorithms must be evaluated in industrial environments that pose challenging conditions to wireless communications due to obstacles, interference, and movement. Therefore, this article compares the performance of LoRaWAN with six different ADR algorithms using a realistic propagation model from an industrial environment. This research uses the framework for LoRa (FLoRa) environment in objective modular network testbed in C++ (Omnet++) to compare the packet delivery ratio and energy consumption with different gateway locations and data traffic. Results show that the algorithms that decrease the link budget in poor channel conditions have better performance in industrial scenarios. Furthermore, overestimating the channel behavior decreases packet delivery.