Water quality in Peru is an increasing concern, particularly in the upper Huarmey watershed, which is affected by heavy metal contamination and untreated wastewater. This study proposes an automated classification approach using three supervised machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF)—to assess the water quality based on the Water Quality Index (WQI) of Peru. The experimental results show that KNN outperforms other methods, reaching an accuracy of 95.2%. The proposed system automates and improves the classification accuracy compared with manual methods based on Microsoft Excel. The methodology, performance metrics, dataset characteristics, and geographical context are detailed to ensure replicability. This algorithm assists decision-makers with environmental monitoring and public health protection.

Water quality in the city of Huarmey-Lima-Peru is assessed through monitoring processes conducted by the National Water Authority (ANA). Traditional formula-based calculations and Excel macros, along with manual data validation, significantly extend the analysis and documentation time for evaluating water quality parameters. This study focuses on classifying water samples into Human Consumption (1-A2) and Animal Drinking (3-D2) categories by developing a machine learning model, specifically utilizing the K-Nearest Neighbors (KNN) algorithm. The primary objective is to enhance accuracy and efficiency in classifying water quality for human and animal consumption, aligning with Peru's Water Quality Index standards (WQI). The implementation of the KNN model demonstrated superior classification performance compared to traditional spreadsheet-based methods, achieving an accuracy of 75% with Excel-based classification and 90% using KNN. Additionally, the research evaluates the performance of KNN against Random Forest (RF) and Support Vector Machine (SVM). This approach significantly improved the speed and accuracy of water sample categorization, benefiting decision-making in water resource management for the Huarmey basin.

Internet Exchange Points (IXPs) constitute critical infrastructure for reducing dependence on external transit and improving network performance; however, their deployment in rural areas of Latin America and the Caribbean (LAC) remains limited, and the adoption of routing security mechanisms is incomplete. This study evaluates three dimensions: the regional evolution of IXPs, observable routing-security evidence among entities associated with IXPs, and the operational performance of a pilot deployment in Junín, Peru. The methodology combined a bibliographic analysis of sources indexed in the ACM Digital Library, IEEE Xplore, and Scopus with data from PeeringDB, Regional Internet Registry (RIR) delegation files, BGP tables, Looking Glass servers, RPKI validation, and MANRS records. For the Junín pilot, a before/after comparison was conducted during March 2024. Between 2016 and 2021, the number of active IXPs in LAC increased from 68 to 98 sites (+44%), while 60 IXPs met the inclusion criteria for the routing-security analysis in the available January–March 2023 extraction records; of these, 30 (50%) showed public evidence of at least one routing-security mechanism among their associated entities. In Junín, the observed mean RTT changed from 137 to 6.9 ms for the Meta measurements and from 138 to 43.8 ms for the Google measurements; because different endpoints were measured before and after deployment, these values represent observed route-specific changes rather than strictly comparable reductions and they do not constitute an isolated causal estimate of the peering effect because the design did not include a control site and, in Meta’s case, the regional cache was part of the same intervention. Netflix was assessed using traceroute because an equivalent ICMP measurement could not be obtained and, therefore, its results were not combined with the ping-based ones. The urban–rural comparison of transit costs relies on approximate values for Lima and Junín compiled from secondary information and pilot documentation, whose original official source could not be traced; it is presented only as an illustrative order of magnitude, not as a verified official or regional billing measurement. Taken together, the results provide site-specific empirical evidence from a rural pilot and a regional framework for studying traffic localization, routing security, and connectivity gaps while avoiding generalizing the site’s results to rural LAC as a whole.

Conventional selfie sticks, typically limited to a maximum reach of 1.5 m, are unable to effectively capture large group photographs or wide-background scenes, frequently resulting in restricted framing and facial cropping. This work presents the design, implementation, and experimental validation of a Bluetooth-controlled mobile robot with five degrees of freedom capable of capturing photographs at distances of up to 10 m using a dual-smartphone master–slave architecture. The proposed system integrates an Arduino Nano microcontroller, an L298N motor driver, three SG90 servo motors, an HC-05 Bluetooth module, and a rechargeable Li-Ion battery mounted on a four-wheel chassis. A master smartphone remotely controls both the mobile platform and the articulated camera arm, while a slave smartphone mounted on the arm captures images through its high-resolution rear camera with real-time video streaming. Experimental evaluations demonstrated 100% command transmission success at distances up to 10 m, automatic emergency stopping within 100–110 ms after Bluetooth disconnection, accurate face centering with minimal servo adjustments, and approximately 45 min of battery autonomy. The proposed open-source, low-cost teleoperated platform extends the effective photographic range from 1.5 m to 10 m while providing potentially improved photographic quality due to rear-camera imaging characteristics and full remote framing control compared with conventional selfie accessories.