The internet of things is an emerging technology that is currently present in most processes and devices, allowing to improve the quality of life of people and facilitating the access to specific information and services. The main purpose of the present article is to offer a general overview of internet of things, based on the analysis of recently published work. The added value of this article lies in the analysis of the main recent publications and the diversity of applications of internet of things technology. As a result of the analysis of the current literature, internet of things technology stands out as a facilitator in business and industrial performance but above all in improving the quality of life. As a conclusion to this document, the internet of things is a technology that can overcome the challenges in terms of security, processing capacity and data mobility, as long as the development related to other technologies follows its expected course.

The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work presents a multivariable algorithm for dynamic channel selection used in cognitive wireless networks. The channel selection is based on the fuzzy analytical hierarchical process (FAHP) method. The selected criteria for choosing the best backup channel are probability of channel availability, estimated channel time availability, signal to noise plus interference ratio, and bandwidth. These criteria are determined by means of a customized Delphi Method and using the FAHP technique; the corresponding weight and significance is calculated for two applications classified as best effort (BE) and real time (RT). The insertion of the fuzzy logic in the AHP algorithm allows better handling of inaccurate information because, as shown the results, consider more options to evaluate in contrast to a conventional AHP. As a difference with related work, the performance of our proposed FAHP method was validated with captured data in experiments realized at the GSM frequency band (824-849 MHz). This is due to the challenge of finding white spaces to communicate in this frequency band. This band represents more disputes in accessing spectral opportunities than other radio frequency (RF) bands because of the high demand for mobile phone communications. The proposed FAHP algorithm has a practical computational complexity and provides an effective frequency-channel selection. This proposed FAHP algorithm presents a new methodology to select and classify the variables based on a modified version of the Delphi method. The results of the proposed method were contrasted numerically with other three methods.

En este artículo se presenta la propuesta de un algoritmo híbrido para la asignación de espectro en redes de radio cognitiva basado en los algoritmos Analytical Hierarchical Process (AHP) y Multi-Criteria Optimization and Compromise Solution (VIKOR), con el objetivo de mejorar el desempeño de la movilidad espectral de los usuarios secundarios en redes de radio cognitiva.Para evaluar el nivel de desempeño del algoritmo propuesto se realiza un análisis comparativo entre  este, el Grey Relational Analysis (GRA) y una asignación de espectro aleatoria (Random). Los dos primeros trabajan con los mismos criterios de decisión: probabilidad de disponibilidad del canal, tiempo estimado de disponibilidad del canal, relación señal a ruido más interferencia y ancho de banda. A diferencia de los trabajos relacionados, la evaluación comparativa se validó a través de una traza de datos reales de ocupación espectral capturados en la banda de frecuencia GSM, que modela el comportamiento real de los usuarios licenciados. En la evaluación de desempeño se utilizaron cinco métricas de evaluación: número promedio acumulado de handoff fallidos, número promedio acumulado de handoff realizados, ancho de banda promedio, retardo promedio acumulado y throughput promedio acumulado.Los resultados del análisis comparativo con los otros dos algoritmos muestran que el algoritmo de handoff AHP-VIKOR propuesto provee el mejor desempeño en la movilidad espectral.

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

In this document is presented the implementation of the programming schedules as a method of lighting control, to perform a total saving and a personalized saving using neural networks. With the acquisition of a series of data about the operation of five lightings located in different parts of a specific house, it was designed a neural network to illuminate it and was implemented this design to the remaining. These neural networks were trained with input vectors; hour of the day, day of the week, holiday Monday's and their respective objective vectors "total saving and personalized saving" with the purpose of evaluating the performance of the neural networks in the optimization of methods for saving electric energy in residential lighting.

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One of the most relevant aspects in the performance of wireless cognitive communications is the interference between users, especially the one that the secondary user can cause to the primary user. A proactive handoff strategy considerably reduces said interference. However, highly accurate prediction models are required. The following article seeks to compare the performance of four algorithms in the spectral occupancy of the primary user during a secondary user's communication. The performance of the algorithms is assessed by using five metrics: handoffs, failed handoffs, bandwidth, delay and throughput. The simulation scenario involves the communication of a secondary user during 10 minutes in a Wi-Fi network.

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