Browsing by Autor "Carlos Lozano-Garzón"

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A Green Routing Mathematical Model for IoT Networks in Critical Energy Environments
(Agora University, 2020) Carlos Lozano-Garzón; Germán A. Montoya; Yezid Donoso
In this paper, we propose a multi-objective mathematical optimization model that is the underlying support for the proposal of a new routing algorithm that aims to extend the lifetime in IoT networks for applications in critical energy environment. The network lifetime is evaluated for three approaches: the Hop Count approach, the Energy Consumption approach, and the Multiobjective approach based on Free Space Loss and the battery energy level of the IoT nodes. After this evaluation, we compared the different approaches in terms of how many transmissions were possible to do under a particular approach until none path cannot be found from an origin node to a destination node. Finally, we conclude that the Multi-objective method was the best strategy for extending the network lifetime since building short distance paths and considering battery level of the IoT nodes every time is, in the long run, a better strategy than just building paths considering nodes with a high battery level or building paths minimizing the number of network hops.
Automatic 3D Urban Installation Generation in Virtual Cities
(2019) Gustavo Alomía; Xun Luo; Claudia Zùñiga; Andrés Navarro; Carlos Lozano-Garzón
This paper presents the integration of procedural modeling and geographic information systems (GIS) for the development of a 3D city model (3DCM), which is basically a computerized digital model of a city. Through a procedural approach based on computer generated animation (CGA) on GIS, the result of this work is 3D virtual modeling of urban areas originally without enough information of urban installations. Our system is capable of 1) detecting areas lacking this type of information, 2) through a sequence of rules-based algorithmic steps, modeling a complete city or part of a city, resulting in a 3D virtual model of the selected area. The final output of our system generates a high-precision and more complete three-dimensional urban environment based on two-dimensional vector data and terrain data in GIS, driven by semantic rules.
Energy-Efficient and Delay Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: Mathematical Optimization, Markov Chains, and Deep Learning Approaches
(Institute of Electrical and Electronics Engineers, 2021) Germán A. Montoya; Carlos Lozano-Garzón; Yezid Donoso
In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to finding a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addition, there are delay-sensitive applications that require establishing communication paths as soon as possible to mitigate low network performance in terms of end-to-end delay, reducing, at the same time, the energy consumption of the network. For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where all network nodes are mobile and minimizing end-to-end delay and energy consumption. This mathematical model would offer significant advantages to evaluate new algorithms due to we could know how far or close are the algorithm results from the optimal values given by the mathematical model. In addition, we propose a prediction distributed routing algorithm based on Markov Chains that takes into account the network mobility in order to find as fast as possible a communication path between a source node and a sink with minimal energy consumption. We also propose a deep learning approach to predict future nodes’ distances in a mobile network to determine if future movements of nodes will cause communication disruptions in paths. Significant findings were obtained when the Markov Chains and Deep Learning approaches were compared in terms of predicting nodes mobility and reducing the delay and the energy consumption in the network. The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is. Finally, to analyze our prediction algorithms considering real online scenarios, we compared it against typical routing algorithms, obtaining promising results in terms of delay and energy consumption in all mobile node scenarios.
Multiple Character Motion Adaptation in Virtual Cities Using Procedural Animation
(2019) Gabriela Salazar; Xun Luo; Andrés Adolfo Navarro Newball; Claudia Zùñiga; Carlos Lozano-Garzón
Every person is unique in his/her size, proportions and style of motion. Reflecting such range of variations in virtual characters is one of the principal objectives to be achieved. While it is obvious that one size dos not fit all, maintaining a huge database of animations for many characters would pose a formidable challenge. By using a systemic approach to animations, we can free the animators from making multiple animations. Our approach allows for generating animation content at runtime to reflect human versatility in virtual characters. The present research aims to use procedural animation combined with inverse kinematics (IK) and simulated physics to have character versatility in an environment where diversity is required with the purpose of having a more realistic virtual scenario, saving time with faster iterations and reducing production costs.
Procedural Animation Generation Technology of Virtual Fish Flock
(2019) Andrea Pilco; Xun Luo; Andrés Adolfo Navarro Newball; Claudia Zùñiga; Carlos Lozano-Garzón
Flocking refers to the collective and coherent motion of a large group of animals. In this paper, a procedural animation generation technology of virtual fish flock is described. In our work, each animated character, symbolized by fish, is implemented as an independent actor that navigates according to its local perception of the environment. Motion of the actor obeys the laws of simulated physics, and a set of behaviors are programmed into it by the animator. Flocking behaviors of separation, alignment and cohesion are be recreated. As such, there is no need to script the motions of each individual fish. These kinds of systems are commonly employed when constructing a large-scale battle or a herd of animals. With this work, we are able to simulate flocking behavior using autonomous agents with simple movement rules.
Procedural Placement of Existing Building Models in Virtual Cities
(2019) Cando Efren; Xun Luo; Andrés Adolfo Navarro Newball; Andrés Navarro; Carlos Lozano-Garzón
Procedural urban scene generation can show a lot of insights about infrastructure, organization and development of a city, and find itself many applications in different fields. On the other hand, in many cases the available information from satellite images and terrestrial dataset is limited, leaving considerable blank spaces in the generated virtual city. How to fill the blank spaces in order to obtain a more realistic scene is thus an important problem to be solved by designers. Designers usually spent enormous time and effort to create three-dimensional (3D) urban models using 3D modeling software. Importing building models manually one by one to cover the blank spaces in a large urban setting can be labor-intensive and also means high cost of time. Enlightened by these observations, this study is aimed at the design and implementation of an algorithm focusing on 1) the identification and positioning of blank spaces in a virtual urban area and 2) the automatic filling of such blank spaces, importing 3D models of existing buildings from a database.
Realistic Behavior of Virtual Citizens through Procedural Animation
(2019) Espinoza Castro Danny Alberto; Xun Luo; Andrés Adolfo Navarro Newball; Claudia Zùñiga; Carlos Lozano-Garzón
Three-dimensional and virtual technologies develop rapidly, tools such as animation of virtual characters, virtual reality, artificial intelligence and computer-generated imagery occupy important spaces in different areas. However, new innovations and technological developments give rise to new challenges for artists and researchers. Several research work have dedicated their efforts to the development of virtual cities, methods to optimize time and information processing during crowd simulation, integration of virtual reality in crowd simulation, among other important milestones. However, the necessary attention has not been paid to the naturalness of the movement that virtual characters perform within these scenes, creating an obstacle to the sensation of the user's presence in this type of virtual environments. Moreover, crowd simulation investigations have been generated on flat ideal terrain, which differs from the reality of different cities that are composed of irregular terrain. Consequently, the use of standard walking animations lead to discrepancies with the real behavior of a human. This paper discusses the use of procedural animation to create realistic movements of a virtual character that can be applied in the different areas of research mentioned, therefore the character within the simulation is able to modify its standard movement without generating new animations and thus optimizing production resources.