�ݺ�ߣshows by User: dhstolfi

�ݺ�ߣshows by User: dhstolfi / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: dhstolfi / Tue, 30 May 2023 11:55:14 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: dhstolfi Improving Pheromone Communication for UAV Swarm Mobility Management /slideshow/improving-pheromone-communication-for-uav-swarm-mobility-management/258129571 main-230530115514-05e8aee6
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. https://doi.org/10.1007/978-3-030-88081-1_17 ]]>
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. https://doi.org/10.1007/978-3-030-88081-1_17 ]]> Tue, 30 May 2023 11:55:14 GMT /slideshow/improving-pheromone-communication-for-uav-swarm-mobility-management/258129571 dhstolfi@slideshare.net(dhstolfi) Improving Pheromone Communication for UAV Swarm Mobility Management dhstolfi In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. https://doi.org/10.1007/978-3-030-88081-1_17 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/main-230530115514-05e8aee6-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. https://doi.org/10.1007/978-3-030-88081-1_17

Improving Pheromone Communication for UAV Swarm Mobility Management from Daniel H. Stolfi

]]> 10 0 https://cdn.slidesharecdn.com/ss_thumbnails/main-230530115514-05e8aee6-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Optimising Autonomous Robot Swarm Parameters for Stable Formation Design /slideshow/optimising-autonomous-robot-swarm-parameters-for-stable-formation-design/258129358 main-230530114300-211c1bb7
Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots. https://doi.org/10.1145/3512290.3528709 ]]>
Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots. https://doi.org/10.1145/3512290.3528709 ]]> Tue, 30 May 2023 11:43:00 GMT /slideshow/optimising-autonomous-robot-swarm-parameters-for-stable-formation-design/258129358 dhstolfi@slideshare.net(dhstolfi) Optimising Autonomous Robot Swarm Parameters for Stable Formation Design dhstolfi Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots. https://doi.org/10.1145/3512290.3528709 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/main-230530114300-211c1bb7-thumbnail.jpg?width=120&height=120&fit=bounds" /> Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots. https://doi.org/10.1145/3512290.3528709

Optimising Autonomous Robot Swarm Parameters for Stable Formation Design from Daniel H. Stolfi

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0 Evaluating Surrogate Models for Robot Swarm Simulations /slideshow/evaluating-surrogate-models-for-robot-swarm-simulations/258129117 ola2023-230530112957-fde25c72
Realistic robotic simulations are computationally demanding, especially when considering large swarms of autonomous robots. This makes the optimisation of such systems intractable, thus limiting the instances' and swarms' size. In this article we study the viability of using surrogate models based on Gaussian processes, Artificial Neural Networks, and simplified simulations, as predictors of the robots' behaviour, when performing formations around a central point of interest. We have trained the predictors and tested them in terms of accuracy and execution time. Our findings show that they can be used as an alternative way of calculating fitness values for swarm configurations which can be used in optimisation processes, increasing the number evaluations and reducing execution times and computing cluster budget. https://doi.org/10.1007/978-3-031-34020-8_17 ]]>
Realistic robotic simulations are computationally demanding, especially when considering large swarms of autonomous robots. This makes the optimisation of such systems intractable, thus limiting the instances' and swarms' size. In this article we study the viability of using surrogate models based on Gaussian processes, Artificial Neural Networks, and simplified simulations, as predictors of the robots' behaviour, when performing formations around a central point of interest. We have trained the predictors and tested them in terms of accuracy and execution time. Our findings show that they can be used as an alternative way of calculating fitness values for swarm configurations which can be used in optimisation processes, increasing the number evaluations and reducing execution times and computing cluster budget. https://doi.org/10.1007/978-3-031-34020-8_17 ]]> Tue, 30 May 2023 11:29:57 GMT /slideshow/evaluating-surrogate-models-for-robot-swarm-simulations/258129117 dhstolfi@slideshare.net(dhstolfi) Evaluating Surrogate Models for Robot Swarm Simulations dhstolfi Realistic robotic simulations are computationally demanding, especially when considering large swarms of autonomous robots. This makes the optimisation of such systems intractable, thus limiting the instances' and swarms' size. In this article we study the viability of using surrogate models based on Gaussian processes, Artificial Neural Networks, and simplified simulations, as predictors of the robots' behaviour, when performing formations around a central point of interest. We have trained the predictors and tested them in terms of accuracy and execution time. Our findings show that they can be used as an alternative way of calculating fitness values for swarm configurations which can be used in optimisation processes, increasing the number evaluations and reducing execution times and computing cluster budget. https://doi.org/10.1007/978-3-031-34020-8_17 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ola2023-230530112957-fde25c72-thumbnail.jpg?width=120&height=120&fit=bounds" /> Realistic robotic simulations are computationally demanding, especially when considering large swarms of autonomous robots. This makes the optimisation of such systems intractable, thus limiting the instances' and swarms' size. In this article we study the viability of using surrogate models based on Gaussian processes, Artificial Neural Networks, and simplified simulations, as predictors of the robots' behaviour, when performing formations around a central point of interest. We have trained the predictors and tested them in terms of accuracy and execution time. Our findings show that they can be used as an alternative way of calculating fitness values for swarm configurations which can be used in optimisation processes, increasing the number evaluations and reducing execution times and computing cluster budget. https://doi.org/10.1007/978-3-031-34020-8_17

Evaluating Surrogate Models for Robot Swarm Simulations from Daniel H. Stolfi

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0 Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates /slideshow/competitive-evolution-of-a-uav-swarm-for-improving-intruder-detection-rates/234668396 pdcostolfi-200528104712
In this paper we present a Predator-Prey approach to enhance the protection of a restricted area using a swarm of Unmanned Aerial Vehicles (UAV). We have chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model for the UAVs and a new model for intruders based on attractive and repulsive forces. After proposing a number of parameters for each mobility model, we have conducted a competitive optimisation of them (Predators and Preys), to achieve a more robust configuration improving the success rate of UAVs when detecting intruders. We have optimised three case studies by performing 30 independent runs of our competitive coevolutionary genetic algorithm and conducted a number of master tournaments using the best specimens obtained for each case study. https://doi.org/10.1109/IPDPSW50202.2020.00094 ]]>
In this paper we present a Predator-Prey approach to enhance the protection of a restricted area using a swarm of Unmanned Aerial Vehicles (UAV). We have chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model for the UAVs and a new model for intruders based on attractive and repulsive forces. After proposing a number of parameters for each mobility model, we have conducted a competitive optimisation of them (Predators and Preys), to achieve a more robust configuration improving the success rate of UAVs when detecting intruders. We have optimised three case studies by performing 30 independent runs of our competitive coevolutionary genetic algorithm and conducted a number of master tournaments using the best specimens obtained for each case study. https://doi.org/10.1109/IPDPSW50202.2020.00094 ]]> Thu, 28 May 2020 10:47:12 GMT /slideshow/competitive-evolution-of-a-uav-swarm-for-improving-intruder-detection-rates/234668396 dhstolfi@slideshare.net(dhstolfi) Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates dhstolfi In this paper we present a Predator-Prey approach to enhance the protection of a restricted area using a swarm of Unmanned Aerial Vehicles (UAV). We have chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model for the UAVs and a new model for intruders based on attractive and repulsive forces. After proposing a number of parameters for each mobility model, we have conducted a competitive optimisation of them (Predators and Preys), to achieve a more robust configuration improving the success rate of UAVs when detecting intruders. We have optimised three case studies by performing 30 independent runs of our competitive coevolutionary genetic algorithm and conducted a number of master tournaments using the best specimens obtained for each case study. https://doi.org/10.1109/IPDPSW50202.2020.00094 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pdcostolfi-200528104712-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper we present a Predator-Prey approach to enhance the protection of a restricted area using a swarm of Unmanned Aerial Vehicles (UAV). We have chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model for the UAVs and a new model for intruders based on attractive and repulsive forces. After proposing a number of parameters for each mobility model, we have conducted a competitive optimisation of them (Predators and Preys), to achieve a more robust configuration improving the success rate of UAVs when detecting intruders. We have optimised three case studies by performing 30 independent runs of our competitive coevolutionary genetic algorithm and conducted a number of master tournaments using the best specimens obtained for each case study. https://doi.org/10.1109/IPDPSW50202.2020.00094

Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates from Daniel H. Stolfi

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0 A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms /slideshow/a-cooperative-coevolutionary-approach-to-maximise-surveillance-coverage-of-uav-swarms/231162443 ccnc2020-slideshare-200331085857
This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently. https://doi.org/10.1109/CCNC46108.2020.9045643 ]]>
This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently. https://doi.org/10.1109/CCNC46108.2020.9045643 ]]> Tue, 31 Mar 2020 08:58:57 GMT /slideshow/a-cooperative-coevolutionary-approach-to-maximise-surveillance-coverage-of-uav-swarms/231162443 dhstolfi@slideshare.net(dhstolfi) A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms dhstolfi This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently. https://doi.org/10.1109/CCNC46108.2020.9045643 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ccnc2020-slideshare-200331085857-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently. https://doi.org/10.1109/CCNC46108.2020.9045643

A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms from Daniel H. Stolfi

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0 Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection /slideshow/optimizing-the-performance-of-an-unpredictable-uav-swarm-for-intruder-detection/229125368 ola-200225133027
In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders. https://doi.org/10.1007/978-3-030-41913-4_4 ]]>
In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders. https://doi.org/10.1007/978-3-030-41913-4_4 ]]> Tue, 25 Feb 2020 13:30:27 GMT /slideshow/optimizing-the-performance-of-an-unpredictable-uav-swarm-for-intruder-detection/229125368 dhstolfi@slideshare.net(dhstolfi) Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection dhstolfi In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders. https://doi.org/10.1007/978-3-030-41913-4_4 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ola-200225133027-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders. https://doi.org/10.1007/978-3-030-41913-4_4

Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection from Daniel H. Stolfi

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0 Ocupación de Aparcamientos y Predicción https://es.slideshare.net/slideshow/ocupacin-de-aparcamientos-y-prediccin/135851041 parking-190312125610
Propuesta ganadora del concurso de reutilización de datos abiertos del Ayuntamiento de Málaga en la categoría Aplicaciones Web. ]]>
Propuesta ganadora del concurso de reutilización de datos abiertos del Ayuntamiento de Málaga en la categoría Aplicaciones Web. ]]> Tue, 12 Mar 2019 12:56:10 GMT https://es.slideshare.net/slideshow/ocupacin-de-aparcamientos-y-prediccin/135851041 dhstolfi@slideshare.net(dhstolfi) Ocupación de Aparcamientos y Predicción dhstolfi Propuesta ganadora del concurso de reutilización de datos abiertos del Ayuntamiento de Málaga en la categoría Aplicaciones Web. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/parking-190312125610-thumbnail.jpg?width=120&height=120&fit=bounds" /> Propuesta ganadora del concurso de reutilización de datos abiertos del Ayuntamiento de Málaga en la categoría Aplicaciones Web.

from Daniel H. Stolfi

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0 Bio-inspired Computing and Smart Mobility /dhstolfi/bioinspired-computing-and-smart-mobility slidesprint-181002104336
This PhD thesis presents a summary of the research work done with the aim of addressing and solving Smart Mobility problems in a smart city context. Several big cities are modeled to be optimized using new evolutionary techniques and the traffic simulator SUMO. Three new architectures, Red Swarm, Green Swarm and Yellow Swarm are proposed, analyzed and used to reduce travel times, greenhouse gas emissions, and fuel consumption of vehicles. A new method for calculating alternative routes for GPS navigators and the prediction of car park occupancy rates are also included in this PhD thesis. Moreover, a novel algorithm for generating realistic traffic flows is developed and tested in different scenarios: working days, Saturdays, and Sundays. Finally, a new family of bio-inspired algorithms based on epigenesis was designed and tested on the Multidimensional Knapsack Problem and used in the Yellow Swarm architecture. https://hdl.handle.net/10630/17299 ]]>
This PhD thesis presents a summary of the research work done with the aim of addressing and solving Smart Mobility problems in a smart city context. Several big cities are modeled to be optimized using new evolutionary techniques and the traffic simulator SUMO. Three new architectures, Red Swarm, Green Swarm and Yellow Swarm are proposed, analyzed and used to reduce travel times, greenhouse gas emissions, and fuel consumption of vehicles. A new method for calculating alternative routes for GPS navigators and the prediction of car park occupancy rates are also included in this PhD thesis. Moreover, a novel algorithm for generating realistic traffic flows is developed and tested in different scenarios: working days, Saturdays, and Sundays. Finally, a new family of bio-inspired algorithms based on epigenesis was designed and tested on the Multidimensional Knapsack Problem and used in the Yellow Swarm architecture. https://hdl.handle.net/10630/17299 ]]> Tue, 02 Oct 2018 10:43:36 GMT /dhstolfi/bioinspired-computing-and-smart-mobility dhstolfi@slideshare.net(dhstolfi) Bio-inspired Computing and Smart Mobility dhstolfi This PhD thesis presents a summary of the research work done with the aim of addressing and solving Smart Mobility problems in a smart city context. Several big cities are modeled to be optimized using new evolutionary techniques and the traffic simulator SUMO. Three new architectures, Red Swarm, Green Swarm and Yellow Swarm are proposed, analyzed and used to reduce travel times, greenhouse gas emissions, and fuel consumption of vehicles. A new method for calculating alternative routes for GPS navigators and the prediction of car park occupancy rates are also included in this PhD thesis. Moreover, a novel algorithm for generating realistic traffic flows is developed and tested in different scenarios: working days, Saturdays, and Sundays. Finally, a new family of bio-inspired algorithms based on epigenesis was designed and tested on the Multidimensional Knapsack Problem and used in the Yellow Swarm architecture. https://hdl.handle.net/10630/17299 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesprint-181002104336-thumbnail.jpg?width=120&height=120&fit=bounds" /> This PhD thesis presents a summary of the research work done with the aim of addressing and solving Smart Mobility problems in a smart city context. Several big cities are modeled to be optimized using new evolutionary techniques and the traffic simulator SUMO. Three new architectures, Red Swarm, Green Swarm and Yellow Swarm are proposed, analyzed and used to reduce travel times, greenhouse gas emissions, and fuel consumption of vehicles. A new method for calculating alternative routes for GPS navigators and the prediction of car park occupancy rates are also included in this PhD thesis. Moreover, a novel algorithm for generating realistic traffic flows is developed and tested in different scenarios: working days, Saturdays, and Sundays. Finally, a new family of bio-inspired algorithms based on epigenesis was designed and tested on the Multidimensional Knapsack Problem and used in the Yellow Swarm architecture. https://hdl.handle.net/10630/17299

Bio-inspired Computing and Smart Mobility from Daniel H. Stolfi

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0 Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms /slideshow/computing-new-optimized-routes-for-gps-navigators-using-evolutionary-algorithms/77960384 gecco2017-170717150205
GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it. http://doi.acm.org/10.1145/3071178.3071193 ]]>
GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it. http://doi.acm.org/10.1145/3071178.3071193 ]]> Mon, 17 Jul 2017 15:02:05 GMT /slideshow/computing-new-optimized-routes-for-gps-navigators-using-evolutionary-algorithms/77960384 dhstolfi@slideshare.net(dhstolfi) Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms dhstolfi GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it. http://doi.acm.org/10.1145/3071178.3071193 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gecco2017-170717150205-thumbnail.jpg?width=120&height=120&fit=bounds" /> GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it. http://doi.acm.org/10.1145/3071178.3071193

Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms from Daniel H. Stolfi

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0 Predicting Car Park Occupancy Rates in Smart Cities /slideshow/predicting-car-park-occupancy-rates-in-smart-cities/76969024 smartct2017-170615104511
In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities. http://dx.doi.org/10.1007/978-3-319-59513-9_11 ]]>
In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities. http://dx.doi.org/10.1007/978-3-319-59513-9_11 ]]> Thu, 15 Jun 2017 10:45:11 GMT /slideshow/predicting-car-park-occupancy-rates-in-smart-cities/76969024 dhstolfi@slideshare.net(dhstolfi) Predicting Car Park Occupancy Rates in Smart Cities dhstolfi In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities. http://dx.doi.org/10.1007/978-3-319-59513-9_11 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smartct2017-170615104511-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities. http://dx.doi.org/10.1007/978-3-319-59513-9_11

Predicting Car Park Occupancy Rates in Smart Cities from Daniel H. Stolfi

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0 Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case Study /slideshow/fine-tuning-of-traffic-in-our-cities-with-smart-panels-the-quito-city-case-study/64648468 gecco2016-160803074606
In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador). http://doi.acm.org/10.1145/2908812.2908868 ]]>
In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador). http://doi.acm.org/10.1145/2908812.2908868 ]]> Wed, 03 Aug 2016 07:46:06 GMT /slideshow/fine-tuning-of-traffic-in-our-cities-with-smart-panels-the-quito-city-case-study/64648468 dhstolfi@slideshare.net(dhstolfi) Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case Study dhstolfi In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador). http://doi.acm.org/10.1145/2908812.2908868 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gecco2016-160803074606-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador). http://doi.acm.org/10.1145/2908812.2908868

Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case Study from Daniel H. Stolfi

]]> 344 5 https://cdn.slidesharecdn.com/ss_thumbnails/gecco2016-160803074606-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 An Evolutionary Algorithm to Generate Real Urban Traffic Flows https://es.slideshare.net/slideshow/an-evolutionary-algorithm-to-generate-real-urban-traffic-flows/54951018 caepia2015-151110124855-lva1-app6892
In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. http://dx.doi.org/10.1007/978-3-319-24598-0_30 ]]>
In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. http://dx.doi.org/10.1007/978-3-319-24598-0_30 ]]> Tue, 10 Nov 2015 12:48:55 GMT https://es.slideshare.net/slideshow/an-evolutionary-algorithm-to-generate-real-urban-traffic-flows/54951018 dhstolfi@slideshare.net(dhstolfi) An Evolutionary Algorithm to Generate Real Urban Traffic Flows dhstolfi In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. http://dx.doi.org/10.1007/978-3-319-24598-0_30 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/caepia2015-151110124855-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. http://dx.doi.org/10.1007/978-3-319-24598-0_30

from Daniel H. Stolfi

]]> 849 71 https://cdn.slidesharecdn.com/ss_thumbnails/caepia2015-151110124855-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case /slideshow/smart-mobility-policies-with-evolutionary-algorithms-the-adapting-info-panel-case/50483000 gecco2015-150713204308-lva1-app6892
In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels. http://doi.acm.org/10.1145/2739480.2754742 ]]>
In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels. http://doi.acm.org/10.1145/2739480.2754742 ]]> Mon, 13 Jul 2015 20:43:08 GMT /slideshow/smart-mobility-policies-with-evolutionary-algorithms-the-adapting-info-panel-case/50483000 dhstolfi@slideshare.net(dhstolfi) Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case dhstolfi In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels. http://doi.acm.org/10.1145/2739480.2754742 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gecco2015-150713204308-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels. http://doi.acm.org/10.1145/2739480.2754742

Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case from Daniel H. Stolfi

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0 Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Utilizando Paneles LED (MAEB'15) https://es.slideshare.net/slideshow/maeb2015/45661348 maeb2015-150310095324-conversion-gate01
En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %.]]>
En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %.]]> Tue, 10 Mar 2015 09:53:24 GMT https://es.slideshare.net/slideshow/maeb2015/45661348 dhstolfi@slideshare.net(dhstolfi) Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Utilizando Paneles LED (MAEB'15) dhstolfi En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/maeb2015-150310095324-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %.

from Daniel H. Stolfi

]]> 952 17 https://cdn.slidesharecdn.com/ss_thumbnails/maeb2015-150310095324-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

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0 Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14) /slideshow/ecofriendly-reduction-of-travel-times-in-european-smart-cities-gecco14/42312885 redswarmgecco2014-141203102523-conversion-gate02
This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities. http://dx.doi.org/10.1145/2576768.2598317 ]]>
This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities. http://dx.doi.org/10.1145/2576768.2598317 ]]> Wed, 03 Dec 2014 10:25:22 GMT /slideshow/ecofriendly-reduction-of-travel-times-in-european-smart-cities-gecco14/42312885 dhstolfi@slideshare.net(dhstolfi) Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14) dhstolfi This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities. http://dx.doi.org/10.1145/2576768.2598317 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redswarmgecco2014-141203102523-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities. http://dx.doi.org/10.1145/2576768.2598317

Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14) from Daniel H. Stolfi

]]> 2037 4 https://cdn.slidesharecdn.com/ss_thumbnails/redswarmgecco2014-141203102523-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (CAEPIA'13) https://es.slideshare.net/slideshow/reducing-gas-emissions-in-smart-cities-by-using-the-red-swarm-architecture-caepia13/42312153 redswarmcaepia2013-141203101330-conversion-gate01
The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem. http://dx.doi.org/10.1007/978-3-642-40643-0_30 ]]>
The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem. http://dx.doi.org/10.1007/978-3-642-40643-0_30 ]]> Wed, 03 Dec 2014 10:13:30 GMT https://es.slideshare.net/slideshow/reducing-gas-emissions-in-smart-cities-by-using-the-red-swarm-architecture-caepia13/42312153 dhstolfi@slideshare.net(dhstolfi) Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (CAEPIA'13) dhstolfi The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem. http://dx.doi.org/10.1007/978-3-642-40643-0_30 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redswarmcaepia2013-141203101330-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem. http://dx.doi.org/10.1007/978-3-642-40643-0_30

from Daniel H. Stolfi

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0 Red Swarm: Smart Mobility in Cities with EAs (GECCO'13) /slideshow/red-swarm-smart-mobility-in-cities-with-eas/42311706 redswarmgecco2013-141203100634-conversion-gate01
This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance. http://dx.doi.org/10.1145/2463372.2463540 ]]>
This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance. http://dx.doi.org/10.1145/2463372.2463540 ]]> Wed, 03 Dec 2014 10:06:34 GMT /slideshow/red-swarm-smart-mobility-in-cities-with-eas/42311706 dhstolfi@slideshare.net(dhstolfi) Red Swarm: Smart Mobility in Cities with EAs (GECCO'13) dhstolfi This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance. http://dx.doi.org/10.1145/2463372.2463540 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redswarmgecco2013-141203100634-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance. http://dx.doi.org/10.1145/2463372.2463540

Red Swarm: Smart Mobility in Cities with EAs (GECCO'13) from Daniel H. Stolfi

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0 https://cdn.slidesharecdn.com/profile-photo-dhstolfi-48x48.jpg?cb=1724779412 www.danielstolfi.com https://cdn.slidesharecdn.com/ss_thumbnails/main-230530115514-05e8aee6-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/improving-pheromone-communication-for-uav-swarm-mobility-management/258129571 Improving Pheromone Co... https://cdn.slidesharecdn.com/ss_thumbnails/main-230530114300-211c1bb7-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/optimising-autonomous-robot-swarm-parameters-for-stable-formation-design/258129358 Optimising Autonomous ... https://cdn.slidesharecdn.com/ss_thumbnails/ola2023-230530112957-fde25c72-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/evaluating-surrogate-models-for-robot-swarm-simulations/258129117 Evaluating Surrogate M...