Swarm Robotics are widely conceived as the development of new computationally efficient tools and... more Swarm Robotics are widely conceived as the development of new computationally efficient tools and techniques aimed at easing and enhancing the coordination of multiple robots towards collaboratively accomplishing a certain mission or task. Among the different criteria under which the performance of Swarm Robotics can be gauged, energy efficiency and battery lifetime have played a major role in the literature. However, technological advances favoring power transfer among robots have unleashed new paradigms related to the optimization of the battery consumption considering it as a resource shared by the entire swarm. This work focuses on this context by elaborating on a routing problem for collaborative exploration in Swarm Robotics, where a subset of robots is equipped with battery recharging functionalities. Formulated as a bi-objective optimization problem, the quality of routes is measured in terms of the Pareto trade-off between the predicted area explored by robots and the risk of battery outage in the swarm. To efficiently balance these conflicting two objectives, a bio-inspired evolutionary solver is adopted and put to practice over a realistic experimental setup implemented in the VREP simulation framework. Obtained results elucidate the practicability of the proposed scheme, and suggest future research leveraging power transfer capabilities over the swarm.
In the last few years the research community has striven to achieve a thorough understanding of t... more In the last few years the research community has striven to achieve a thorough understanding of the brain activity when the subject under analysis undertakes both mechanical tasks and purely mental exercises. One of the most avant-garde approaches in this regard is the discovery of connectivity patterns among different parts of the human brain unveiled by very diverse sources of information (e.g. magneto- or electro-encephalography – M/EEG, functional and structural Magnetic Resonance Imaging – fMRI and sMRI, or positron emission tomography – PET), coining the so-called brain connectomics discipline. Surprisingly, even though contributions related to the brain connectome abound in the literature, far too little attention has been paid to the exploitation of such complex spatial-temporal patterns to classify the task performed by the subject while brain signals are being registered. This manuscript covers this research niche by elaborating on the extraction of topological features from the graph modeling the brain connectivity under different tasks. By resorting to public information from the Human Connectome Project, the work will show that a selected subset of topological predictors from M/EEG connectomes suffices for accurately predicting (with average accuracy scores of up to 95%) the task performed by the subject at hand, further insights given on their predictive power when the M/EEG connectivity is inferred over different frequency bands.
Millimeter wave (mmWave) communications have been postulated as one of the most disruptive techno... more Millimeter wave (mmWave) communications have been postulated as one of the most disruptive technologies for future 5G systems. Among mmWave bands the 60-GHz radio technology is specially suited for ultradense small cells and mobile data offloading scenarios. Many challenges remain to be addressed in mmWave communications but among them deafness, or misalignment between transmitter and receivers beams, and interference management lie among the most prominent ones. In the recent years, scenarios considering negligible interference on mmWave resource allocation have been rather common in literature. To this end, interestingly, many open issues still need to be addressed such as the applicability of noise-limited regime for mmWave. Furthermore, in mmWave the beam-steering mechanism imposes a forced silence period, in the course of which no data can be conveyed, that should not be neglected in throughput/delay calculations. This paper introduces mmWave enabled Small Cell Networks (SCNs) ...
Since the advent of Telecommunication networks in the early 60’s, routing has become a recurrent ... more Since the advent of Telecommunication networks in the early 60’s, routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources, stringent Quality of Service (QoS) constraints and unmanageable network scales (size, speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency, loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firey Algorithm, two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms, with emphasis on their convergence speed and s...
The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed gr... more The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost‐affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also defi...
The solutions to many optimization paradigms arising from different application domains can be mo... more The solutions to many optimization paradigms arising from different application domains can be modeled as a tree graph, in such a way that nodes represent the variables to be optimized and edges evince topological relationships between such variables. In these problems the goal is to infer an optimal tree graph interconnecting all nodes under a measure of topological fitness, for which a wide portfolio of exact and approximative solvers have hitherto been reported in the related literature. In this context a research line of interest in the last few years has been focused on the derivation of solution encoding strategies suited to deal with the topological constraints imposed by tree graph configurations, particularly when the encoded solution undergoes typical operators from Evolutionary Computation. Almost all contributions within this research area focus on the use of standard crossover and mutation operators from Genetic Algorithms onto the graph topology beneath encoded individuals. However, the pace at which new evolutionary operators have emerged from the community has grown much sharply during the last decade. This manuscript elaborates on the topological heritability of the so-called Dandelion tree encoding approach under non-conventional operators. This experimental application-agnostic-based study gravitates on the topological transmission of Dandelion-encoded solutions under a certain class of multi-parent crossover operators that lie at the core of the family of $$(\mu +1)$$(μ+1) evolution strategies and in particular, the so-called Harmony Search algorithm. Metrics to define topological heritability and respect will be defined and evaluated over a number of convergence scenarios for the population of the algorithm, from which insightful conclusions will be drawn in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.
Swarm Robotics are widely conceived as the development of new computationally efficient tools and... more Swarm Robotics are widely conceived as the development of new computationally efficient tools and techniques aimed at easing and enhancing the coordination of multiple robots towards collaboratively accomplishing a certain mission or task. Among the different criteria under which the performance of Swarm Robotics can be gauged, energy efficiency and battery lifetime have played a major role in the literature. However, technological advances favoring power transfer among robots have unleashed new paradigms related to the optimization of the battery consumption considering it as a resource shared by the entire swarm. This work focuses on this context by elaborating on a routing problem for collaborative exploration in Swarm Robotics, where a subset of robots is equipped with battery recharging functionalities. Formulated as a bi-objective optimization problem, the quality of routes is measured in terms of the Pareto trade-off between the predicted area explored by robots and the risk of battery outage in the swarm. To efficiently balance these conflicting two objectives, a bio-inspired evolutionary solver is adopted and put to practice over a realistic experimental setup implemented in the VREP simulation framework. Obtained results elucidate the practicability of the proposed scheme, and suggest future research leveraging power transfer capabilities over the swarm.
In the last few years the research community has striven to achieve a thorough understanding of t... more In the last few years the research community has striven to achieve a thorough understanding of the brain activity when the subject under analysis undertakes both mechanical tasks and purely mental exercises. One of the most avant-garde approaches in this regard is the discovery of connectivity patterns among different parts of the human brain unveiled by very diverse sources of information (e.g. magneto- or electro-encephalography – M/EEG, functional and structural Magnetic Resonance Imaging – fMRI and sMRI, or positron emission tomography – PET), coining the so-called brain connectomics discipline. Surprisingly, even though contributions related to the brain connectome abound in the literature, far too little attention has been paid to the exploitation of such complex spatial-temporal patterns to classify the task performed by the subject while brain signals are being registered. This manuscript covers this research niche by elaborating on the extraction of topological features from the graph modeling the brain connectivity under different tasks. By resorting to public information from the Human Connectome Project, the work will show that a selected subset of topological predictors from M/EEG connectomes suffices for accurately predicting (with average accuracy scores of up to 95%) the task performed by the subject at hand, further insights given on their predictive power when the M/EEG connectivity is inferred over different frequency bands.
Millimeter wave (mmWave) communications have been postulated as one of the most disruptive techno... more Millimeter wave (mmWave) communications have been postulated as one of the most disruptive technologies for future 5G systems. Among mmWave bands the 60-GHz radio technology is specially suited for ultradense small cells and mobile data offloading scenarios. Many challenges remain to be addressed in mmWave communications but among them deafness, or misalignment between transmitter and receivers beams, and interference management lie among the most prominent ones. In the recent years, scenarios considering negligible interference on mmWave resource allocation have been rather common in literature. To this end, interestingly, many open issues still need to be addressed such as the applicability of noise-limited regime for mmWave. Furthermore, in mmWave the beam-steering mechanism imposes a forced silence period, in the course of which no data can be conveyed, that should not be neglected in throughput/delay calculations. This paper introduces mmWave enabled Small Cell Networks (SCNs) ...
Since the advent of Telecommunication networks in the early 60’s, routing has become a recurrent ... more Since the advent of Telecommunication networks in the early 60’s, routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources, stringent Quality of Service (QoS) constraints and unmanageable network scales (size, speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency, loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firey Algorithm, two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms, with emphasis on their convergence speed and s...
The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed gr... more The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost‐affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also defi...
The solutions to many optimization paradigms arising from different application domains can be mo... more The solutions to many optimization paradigms arising from different application domains can be modeled as a tree graph, in such a way that nodes represent the variables to be optimized and edges evince topological relationships between such variables. In these problems the goal is to infer an optimal tree graph interconnecting all nodes under a measure of topological fitness, for which a wide portfolio of exact and approximative solvers have hitherto been reported in the related literature. In this context a research line of interest in the last few years has been focused on the derivation of solution encoding strategies suited to deal with the topological constraints imposed by tree graph configurations, particularly when the encoded solution undergoes typical operators from Evolutionary Computation. Almost all contributions within this research area focus on the use of standard crossover and mutation operators from Genetic Algorithms onto the graph topology beneath encoded individuals. However, the pace at which new evolutionary operators have emerged from the community has grown much sharply during the last decade. This manuscript elaborates on the topological heritability of the so-called Dandelion tree encoding approach under non-conventional operators. This experimental application-agnostic-based study gravitates on the topological transmission of Dandelion-encoded solutions under a certain class of multi-parent crossover operators that lie at the core of the family of $$(\mu +1)$$(μ+1) evolution strategies and in particular, the so-called Harmony Search algorithm. Metrics to define topological heritability and respect will be defined and evaluated over a number of convergence scenarios for the population of the algorithm, from which insightful conclusions will be drawn in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.
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Papers by Nekane Bilbao