Bio-inspired route estimation in cognitive radio networks

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms

Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRE

Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives

Analysis and assessment software for multi-user collaborative cognitive radio networks

Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features

Spectrum sharing in cognitive radio networks

Cognitive radio networks are the next step to tackle scarcity in wireless networks given the increasing demand of radioelectric spectrum where the proposed solution is to share said resource to improve this situation. In the present article, a review of the current state of spectrum sharing in cognitive radio networks. To achieve this purpose, the articles published over the last 4 years on the matter were reviewed including topics such as mobile networks and TV. Some studies and simulations proposed to share the spectrum is shown. The current state of the studies reveals that there has been significant progress in this research area yet it is necessary to continue similar studies and set in motion different schemes

Spectral opportunity selection based on the hybrid algorithm Ahp-Electre

Debido a la creciente demanda de espectro y al rápido desarrollo de aplicaciones inalámbricas, tecnologías como la radio cognitiva permiten usar el espectro de manera eficiente. El objetivo del presente artículo es diseñar un algoritmo capaz de elegir el mejor canal para la transmisión de datos utilizando métodos cuantitativos que puedan modificar el comportamiento en términos de cambios en los parámetros de calidad del canal. Para lograr esta tarea, se diseñó un algoritmo híbrido de toma de decisiones que combina algoritmos AHP y ajusta los pesos de cada parámetro del canal, utilizando una tabla de prioridad. El algoritmo ELECTRE se encarga de procesar la información de cada canal con una matriz de peso para entregar el resultado más favorable para el tipo de datos a transmitir. Los resultados muestran un rendimiento satisfactorio del algoritmo AHP-ELECTRE en comparación con alternativas similares.Due to the growing demand for spectrum and the rapid development of wireless applications, technologies such as cognitive radio allow to use the spectrum efficiently. The objective of the present article is to design an algorithm capable of choosing the best channel for data transmission using quantitative methods that can modify the behavior in terms of changes in the quality parameters of the channel. To achieve this task, a hybrid decision-making algorithm is designed that combines AHP algorithms and adjusts weights of each parameter of the channel, using a priority table. The ELECTRE algorithm is in charge of processing the information from each channel with weight matrix to deliver the most favorable result for the type of data to be transmitted. The results show a satisfying performance of the AHP-ELECTRE algorithm when compared to similar alternatives.Universidad Distrital Francisco José de Calda

Spectrum sharing in Cognitive Radio Networks

Las redes de radio cognitivas son el siguiente paso para abordar la escasez de redes inalámbricas dado el aumento demanda de espectro radioeléctrico donde la solución propuesta es compartir dicho recurso para mejorar este situación. En el presente artículo, una revisión del estado actual del espectro compartido en las redes de radio cognitivas. Para lograr este propósito, se revisaron los artículos publicados en los últimos 4 años sobre la materia incluyendo temas como redes móviles y televisión. Algunos estudios y simulaciones propuestos para compartir el espectro son mostrados. El estado actual de los estudios revela que se han producido importantes avances en esta área de investigación sin embargo, es necesario continuar estudios similares y poner en marcha diferentes esquemas.Cognitive radio networks are the next step to tackle scarcity in wireless networks given the increasing demand of radioelectric spectrum where the proposed solution is to share said resource to improve this situation. In the present article, a review of the current state of spectrum sharing in cognitive radio networks. To achieve this purpose, the articles published over the last 4 years on the matter were reviewed including topics such as mobile networks and TV. Some studies and simulations proposed to share the spectrum are shown. The current state of the studies reveals that there has been significant progress in this research area yet it is necessary to continue similar studies and set in motion different schemes

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

A pervasive prediction model for vehicular ad-hoc network (VANET)

The growth of city traffic has contributed to severe traffic congestion and traffic accidents in the most of the cities in the world. Since people’s travel demand rise at a rate usually greater than the addition of road capacity to lead many other issues, such as environmental problems and the quality of life. Intelligent Transportation System (ITS) is committed to solving the worsening traffic problems. Wide deployment of such ITS can eventually provide more dynamic, real-time and efficient solutions to transportation problems. ITS uses a variety of high technologies, especially electronic information technology and data communications technology to improve road traffic efficiency, road traffic safety and environmental protection. A number of researchers have depended on the wireless mobile communication to improve data collection and utilisation. The data could be used for early warning and forecasting traffic conditions in real-time. The benefit of wireless mobile communication research, especially Car to Car (C2C) communication is to abandon the expensive wireline-deployed and central processing units. Through the interconnection of many personal mobile devices, a low- cost freely extended, high-performance and parallel system can be formed. Car to Car communication can make possible efficient and reliable data transmission by wireless links in a traffic area. It is based on principles of mobile ad-hoc network (MANET) and applies to the domain of vehicles, being Vehicular ad-hoc network (VANET) which is a key component of ITS. The C2C communication system has become essential for driving safety and comfort and also for improving road condition. Also, the traffic prediction system is also an important part of ITS, traffic condition prediction can be regarded as an extension application of VANET. It provides traffic condition in advance via a variety of prediction models and helps the people make better driving safety, travel decisions and route selections regarding departure or driving time. The challenge of wireless traffic prediction technology is the uncertainty of traffic and real-time traffic data collection. It is widely known that urban transport system is a participatory, time-varying and complex nonlinear system. This uncertainty comes not only from the natural causes, such as seasonal and weather factors, but also from human factors, such as traffic accidents, emergencies and driver’s behaviour. In particular, the short-term traffic prediction is more affected by random interference factors. Current wireless traffic prediction research is usually based on a combination of wireless technology and traditional prediction model. The predictable traffic conditions include travel speed, travel time, traffic density, traffic accident, congestion level. However, in a large network environment, as the number of nodes increases, the transmission performance degrades and the prediction accuracy decreases because the prediction model does not obtain enough data. In this thesis, a novel traffic prediction framework (PPM-C2C) is proposed – Pervasive Prediction Model (PPM) based on the C2C communication. The framework utilises ad-hoc data via C2C communications for a short time traffic prediction in each car. This project builds and investigates the behaviour of a pervasive traffic simulation model in Ad-hoc network, with a particular part of it embedded into each vehicle’s equipment. It includes the data collection, aggregation and application aim to be running in all individual cars so that cars have up to date information on the traffic at all times. Moreover, those cars could predict the traffic conditions of a road section in a short time through the proposed prediction framework, especially travel speed prediction. When the car receives the current traffic information about other vehicles, the prediction system will incorporate the information, analyse the data and predict the traffic conditions of this road section for a future time. The design does not depend on any roadside communications infrastructure. It is a simple and flexible car communication and processing technology to collect real-time traffic information. This process will be aided by car to car wireless communication technology available nowadays. To achieve this goal, a mobility model adapted to VANET needs to be generated that a realistic city scenario based on the actual traffic traces is carried out through simulation. Based on this, we investigate the necessary influencing factors for predicted results. The simulation results illustrate that the prediction model can be applied to wireless network environment for a short time prediction, and our results demonstrate the viability and effectiveness of the proposed prediction framework over Car to Car communications. Furthermore, the wireless environment and derived factors can result in decreased application performance