This article analyzes the wireless coverage problem of remote regions with low user density and propagation problems due to irregular terrain and high tree density. The use of a wireless mesh network is proposed as a solution where user data is transmitted directly to a base station when it is possible or relayed through other nodes in a multi-hop manner when there is not a direct link. The mesh topology uses short links with higher capacity instead of long links with low capacity. We analyze radio propagation and MAC protocols, including random sources, queues, and packet delays. The article is based on the coverage of a realistic rural region near Medellin, a mid size city in Colombia, and it shows that a wireless mesh network is an alternative to a typical cell based solution. It includes propagation analysis and MAC protocol behavior, so it is not limited to a radio coverage problem but also traffic, scheduling, and routing analysis

The iteration human with the environment depends on the ability to use the limbs for manipulating objects effects, the development of these skills in robots is a broad field where they can research and implementations, one of which is the development platforms robotics for operation by sensors that provide good performance robot control, even if the user has certain limitations drive. This paper presents an alternative of a Human Machine Interface (HMI), which is based on the use of the Microsoft Kinect sensor, this system allows the user to control HMI realtime SCARA robotic arm, by estimating movements upper extremity of the operator using depth images and assuming a starting position. To implement the control of the robotic arm using the Kinect sensor as this through its libraries OpenNI, allows solutions in C# using the natural interaction and giving track joints associated with left and right hand users can rotate motors robot and control the behavior of the SCARA robot by simply moving your arms.

Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management systems (HEMS) are positioning themselves as an efficient alternative by integrating artificial intelligence to improve their accuracy. Predictive algorithms that provide accurate data on the future behavior of energy consumption and appliance usage time are required in these HEMS to achieve this goal. This study presents a predictive model based on recurrent neural networks with long short-term memory (LSTM), known to capture nonlinear relationships and long-term dependencies in time series data. The model predicts individual and total household energy consumption and appliance usage time. Training data were collected for 12 months from an HEMS installed in a typical Colombian house, using smart meters developed in this research. The model’s performance is evaluated using the mean squared error (MSE), reaching a value of 0.0168 kWh². The results confirm the effectiveness of HEMS and demonstrate that the integration of LSTM-based predictive models can significantly improve energy efficiency and optimize household energy consumption.

Optical transport networks (OTN) must be prepared in terms of better resource utilization, for accommodating unicast and multicast traffic together. Light-trees have been proposed for supporting multicast connections in OTN. Nevertheless when traffic grooming is applied in light-trees, resources can be underutilized as traffic can be routed to undesirable destinations in order to avoid optical-electrical-optical (OEO) conversions. In this paper, a novel architecture named S/G light- tree for supporting unicast/multicast connections is proposed. The architecture allows traffic dropping and aggregation in different wavelengths without performing OEO conversions. A heuristic that routes traffic demands using less wavelengths by taking advantage of the proposed architecture is designed as well. Simulation results show that the architecture can minimize the number of used wavelengths and OEO conversions when compared to light-trees.

Non-Intrusive Load Monitoring (NILM) enables appliance-level energy analysis from aggregated electrical signals, offering valuable insights for smart energy systems. While most NILM research focuses on high-resource environments, this study evaluates the feasibility of deploying NILM algorithms on constrained edge computing platforms. Two representative models for event detection and for energy disaggregation were trained on a high-end PC and tested on both the PC and two edge devices. A modular software framework using a virtual container and virtual environments ensured reproducibility across platforms. Experiments using datasets under simulated real-time streaming conditions revealed that although all devices achieved consistent detection, classification, and disaggregation performance, edge platforms struggled with real-time inference due to processing latency and memory limitations. This study presents a detailed comparison of execution time, resource usage, and model performance, highlighting the trade-offs associated with NILM deployment on embedded systems and proposing future directions for optimization and integration into smart grids.

The increasing demand of fixed and mobile applications, and considering that smart grid imposes new requirements to the access networks, in this paper we present an optical access architecture to support home multiservice including smart grid applications. We propose a migration path based in a WDM passive optical network (PON) architecture for the next 10 years. Moreover, we compare the performance of each stage in terms of data bit rate requirements, bit error rate (BER) and jitter.