The measurement of environmental variables has become a daily problem in recent years. However, the equipment commonly used for these measurements is expensive and bulky, and therefore, it is not possible to have enough spatial resolution. In addition, many of the measurement methods do not provide real-time information to deliver to citizens in a timely manner. In some works, these issues have been handled through the deployment of WSN based on low cost technologies. The improvement of the spatial and temporal resolution implies the increase in the amount of information to be transmitted and stored. For this reason, in this paper is presented a method for data reduction through a dynamic sub-sampling of the measured variable, data fusion from several sensors for the same variable, and data scaling taking into account the the variables range. The reduction of data is implemented to save energy, reduce the transmission time, keep the channel available, and save storage space. The method is validated using a low-cost monitoring station that combines environmental, particulate matter, gas, electromagnetic radiation, and inertial sensors to be transmitted in a 50-byte reduced packet using a LoRa network. The subsampling adjustment was developed for the PM signal. The results show a reduction in the volume of stored data and the relevant information is not affected. The transmitted data packet can be reduced from 96 to 50 bytes, and sampling can be reduced to 4% of the original sampling without affecting the trend of the PM information.

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction $47.25\%$ reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

The development of autonomous vessels and unmanned surface vehicles (USVs) has generated great interest in the scientific community due to their potential and advantages for use in various environments and applications. Several literature review papers have been produced from different perspectives, contributing to a better understanding of the topic and to the analysis of advances, challenges, and trends. We hypothesize that the greatest attention has been focused on the development of high-impact applications in the maritime sector. Additionally, we depart from the need to investigate the potential and advances of USVs in fluvial environments, which involve particular operating conditions, where there are different socio-environmental conditions and restrictions in terms of access to conventional energy sources and communication systems. In this sense, the main objective of this work is to study USVs in the particular context of small craft. The search for records was conducted in Scopus and Web of Science databases, covering studies published from 2000 to 16 May 2024. The methodology employed was based on the PRISMA 2020 guidelines, which is a widely recognized protocol that ensures quality and rigor in systematic reviews and bibliometric analyses. To optimize the data collection and selection process, the semaphore technique was additionally implemented, allowing for an efficient categorization of the studies found. This combined methodological approach facilitated a systematic and transparent evaluation of the literature. This study was developed based on three research questions about the evolution of research topics, areas of application, and types of algorithms related to USVs. The study of the evolution of works on USVs was carried out based on the results of the meta-analysis generated with the Bibliometrix tool. The study of applications and developments was carried out based on information obtained from the papers for six study categories: application environment, level of autonomy, application area, algorithm typology, methods, and electronic devices used. For each of the 387 papers identified in the databases, labeling was performed for the 359 screened papers with six study categories according to the availability of information in the title and In the categories application sector, autonomy level, application area and algorithm type/task, it was identified that most studies are oriented toward the maritime sector, the developments to achieve full autonomy for USVs, the development of designs or algorithms at the modeling and simulation level, and the development and implementation of algorithms for the GNC subsystems. Nevertheless, this research has revealed a much wider range of environments and applications beyond maritime, military, and commercial sectors. In addition, from the mapping of the types of algorithms used in the GNC architecture, the study provides information that can be used to guide the design of the subsystems that enable USV autonomy for civilian use in restricted environments.

The Internet of Things continues to set the pace of many aspects of the scientific and technological development worldwide. Moreover, it is expected to do so, at least, for the next ten years, according to forecasts that predict trillions of devices connected to the Internet [1]. In addition to influencing different factors of people’s daily lives, this new vision of the world poses a series of challenges and opportunities that have an effect on general aspects of the economy and politics.

The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository.