Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

Effect of event-based sensing on IoT node power efficiency. Case study: air quality monitoring in smart cities

The predicted growth of urban populations has prompted researchers and administrations to improve services provided to citizens. At the heart of these services are wireless networks of multiple different sensors supported by the Internet of Things. The main purpose of these networks is to provide sufficient information to achieve more intelligent transport, energy supplies, social services, public environments (indoor and outdoor) and security, etc. Two major technological advances would improve such networks in Smart Cities: efficient communication between nodes and a reduction in each node's power consumption. The present paper analyses how event-based sampling techniques can address both challenges. We describe the fundamentals of the triggering mechanisms that characterise Send-on-Delta, Send-on-Area, Send-on-Energy and Send-on-Prediction techniques to restrict the number of transmissions between the sensor node and the supervision or monitoring node without degrading tracking of the sensed variable. At the same time, these aperiodic techniques reduce consumption by sensor node electronic devices. In order to quantify the energy savings, we evaluate the increase achieved in the average lifetime of sensor node batteries. The data provided by Smart City tools in the city of Santander (Spain) were selected to conduct a case study of the main pollutants that determine city air quality: SO2 , NO2 , O3 and PM10 . We conclude that event-based sensing techniques can yield up to 50% savings in sensor node consumption compared to classical periodic sensing techniques

SMCTC : sequential Monte Carlo in C++

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation

SMCTC: Sequential Monte Carlo in C++

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation.

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward

Modelado de transmisión eficiente de datos para eventos multivariantes basados en umbral

This doctoral thesis delves into the optimization of communications in sensor networks for a specific purpose: to evaluate threshold-based events that depend on multiple distributed variables. This motivation is behind the detailed research presented here in the form of a compendium of papers. The developed work is structured in 3 scientific contributions in articles. Out of those 3 contributions, the most theoretical work has been described in 2 of them, leaving the third article for the presentation of a methodological support tool with great scientific impact and relevance in this doctoral thesis. Due to the two theoretical and large–scale contributions in the proposed field, a solution is proposed which is stated as an hypotheses. The first contribution is the mathematical foundations for modelling data reduction in the sensor network and measuring its influence on the quality of the event evaluation. For this purpose, a set of functions and parameters is defined. This logic modifies the cardinality of the mathematical domains in which information is defined in order to save traffic. Specific metrics that consider the time delays in the state changes of the evaluated condition are also defined. The second contribution is an adaptive algorithm that, taking into account the logical context of the system information, parameterizes the proposed model at runtime. As a result, this technique maximizes traffic reduction and minimizes error in the evaluation of the event simultaneously, obtaining promising results. As a methodological contribution, a procedure for generating pseudo-realistic random signals is also described, a useful tool for easily obtaining large datasets suitable for experimentation, which has been applied in the described contributions.Esta tesis doctoral profundiza en la optimización de las comunicaciones en redes de sensores con un propósito específico: evaluar eventos basados en umbral que dependen de múltiples variables distribuidas. Con esta motivación se desarrolla la investigación detallada aquí en forma compendio de artículos. El trabajo desarrollado se estructura en 3 aportaciones científicas en artículos. De esas 3 aportaciones, el trabajo en su vertiente más teórica se desarrolla en 2 de ellas, quedando el tercer artículo para la presentación de una herramienta de soporte metodológico con gran impacto científico y de relevancia en esta tesis doctoral. Gracias a las dos aportaciones teóricas y de gran calado en el ámbito propuesto se propone una solución que se plantea en forma de hipótesis. La primera aportación son los fundamentos matemáticos para modelar la reducción de datos en la red de sensores y medir su incidencia en la calidad de la evaluación del evento. Para ello define una serie de funciones y parámetros que alteran la cardinalidad de los dominios matemáticos en los que se define la información, así como métricas específicas que tienen en cuenta los desfases temporales en los cambios de estado de la condición evaluada. La segunda aportación es un algoritmo adaptativo que, considerando el contexto lógico de la información del sistema, parametriza el modelo propuesto en tiempo de ejecución. Como resultado, esta técnica maximiza la reducción de tráfico y minimiza el error en la evaluación del evento simultáneamente, obteniendo resultados prometedores. Como tercera aportación se describe también un procedimiento para generar señales aleatorias pseudo–realistas, una herramienta útil para disponer fácilmente de grandes conjuntos de datos adecuados para experimentación, que ha sido utilizada en las aportaciones descritas

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation.