@article{
 title = {Correlating node centrality metrics with node resilience in self-healing systems with limited neighbourhood information},
 type = {article},
 year = {2025},
 keywords = {Centrality metrics,Correlation,Limited hop information,Network topology,Self-healing system},
 pages = {107553},
 volume = {163},
 websites = {https://www.sciencedirect.com/science/article/pii/S0167739X2400517X},
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 citation_key = {RODRIGUEZ2025107553},
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 abstract = {Resilient systems must self-heal their components and connections to maintain their topology and function when failures occur. This ability becomes essential to many networked and distributed systems, e.g., virtualisation platforms, cloud services, microservice architectures and decentralised algorithms. This paper builds upon a self-healing approach where failed nodes are recreated and reconnected automatically based on topology information, which is maintained within each node’s neighbourhood. The paper proposes two novel contributions. First, it offers a generic method for establishing the minimum size of a network neighbourhood to be known by each node in order to recover the system’s component interconnection topology under a certain probability of node failure. This improves the previous proposal by reducing resource consumption, as only local information is communication and stored. Second, it adopts analysis techniques from complex networks theory to correlate a node’s recovery probability with its closeness centrality within the self-healing system. This allows strengthening a system’s resilience by analysing its topological characteristics and rewiring weakly-connected nodes. These contributions are supported by extensive simulation experiments on different systems with various topological characteristics. Obtained results confirm that nodes which propagate their topology information to more neighbours are more likely to be recovered; while requiring more resources. The proposed contributions can help practitioners to: identify the most fragile nodes in their distributed systems; consider corrective measures by increasing each node’s connectivity; and, establish a suitable compromise between system resilience and costs.},
 bibtype = {article},
 author = {Rodríguez, Arles and Diaconescu, Ada and Rodríguez, Johan and Gómez, Jonatan},
 doi = {https://doi.org/10.1016/j.future.2024.107553},
 journal = {Future Generation Computer Systems}
}

Resilient systems must self-heal their components and connections to maintain their topology and function when failures occur. This ability becomes essential to many networked and distributed systems, e.g., virtualisation platforms, cloud services, microservice architectures and decentralised algorithms. This paper builds upon a self-healing approach where failed nodes are recreated and reconnected automatically based on topology information, which is maintained within each node’s neighbourhood. The paper proposes two novel contributions. First, it offers a generic method for establishing the minimum size of a network neighbourhood to be known by each node in order to recover the system’s component interconnection topology under a certain probability of node failure. This improves the previous proposal by reducing resource consumption, as only local information is communication and stored. Second, it adopts analysis techniques from complex networks theory to correlate a node’s recovery probability with its closeness centrality within the self-healing system. This allows strengthening a system’s resilience by analysing its topological characteristics and rewiring weakly-connected nodes. These contributions are supported by extensive simulation experiments on different systems with various topological characteristics. Obtained results confirm that nodes which propagate their topology information to more neighbours are more likely to be recovered; while requiring more resources. The proposed contributions can help practitioners to: identify the most fragile nodes in their distributed systems; consider corrective measures by increasing each node’s connectivity; and, establish a suitable compromise between system resilience and costs.

@article{
 title = {Innovameter: Agent-based modeling of innovation determinants in American and European countries},
 type = {article},
 year = {2025},
 pages = {1-26},
 volume = {20},
 id = {5f5d174b-1104-3ea5-87f4-b7fbc9e6570e},
 created = {2025-01-31T22:11:33.639Z},
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 last_modified = {2025-01-31T22:11:42.684Z},
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 abstract = {This article discusses the dynamics of innovation in America and Europe, focusing on variables such as access to technology, education, and life expectancy. To do this, the article proposes an agent-based model called the Innovameter. The dependent variable is the Global Innovation Index. The paper focuses on data analysis through correlation analysis and multiple hierarchical regressions to determine the contribution of specific variables related to the pillars of the Global Innovation Index and indicators of the Human Development Index. After analyzing the data, an agent-based model was built to parameterize these main variables by defining two levels of abstraction: at the global level, there is the country, where birth rates, life expectancy, ICT use, and research and development are defined. At the local level, we define the individuals who have an age, years of schooling, and income. A series of experiments were conducted by selecting data from 30 countries. From the results of the experiments, a nonparametric correlation analysis was performed, and correlation indices were obtained indicating a relationship between the predicted outcomes and the outcomes in the global index. The proposed model aims to provide suggestions on how the different variables can become the norm in most of the countries studied.},
 bibtype = {article},
 author = {Robayo-Acuña, Paula and Ruíz-Castro, Iván Ricardo and Rodríguez, Arles and Gaitán-Angulo, Mercedes and Gómez-Caicedo, Melva Inés},
 doi = {10.1371/journal.pone.0313756},
 journal = {PLoS ONE},
 number = {1 January}
}

This article discusses the dynamics of innovation in America and Europe, focusing on variables such as access to technology, education, and life expectancy. To do this, the article proposes an agent-based model called the Innovameter. The dependent variable is the Global Innovation Index. The paper focuses on data analysis through correlation analysis and multiple hierarchical regressions to determine the contribution of specific variables related to the pillars of the Global Innovation Index and indicators of the Human Development Index. After analyzing the data, an agent-based model was built to parameterize these main variables by defining two levels of abstraction: at the global level, there is the country, where birth rates, life expectancy, ICT use, and research and development are defined. At the local level, we define the individuals who have an age, years of schooling, and income. A series of experiments were conducted by selecting data from 30 countries. From the results of the experiments, a nonparametric correlation analysis was performed, and correlation indices were obtained indicating a relationship between the predicted outcomes and the outcomes in the global index. The proposed model aims to provide suggestions on how the different variables can become the norm in most of the countries studied.

@article{
 title = {Improving data collection in complex networks with failure-prone agents via local marking},
 type = {article},
 year = {2019},
 keywords = {Complex networks,Data collection,Failure-prone mobile agents,Local marking},
 pages = {5081-5089},
 volume = {36},
 websites = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-179053},
 month = {5},
 day = {14},
 id = {d134c8c0-8066-335f-8868-9520065cf30a},
 created = {2019-11-06T18:40:53.971Z},
 file_attached = {true},
 profile_id = {48596512-087a-3be6-8e68-21c90329c4c9},
 last_modified = {2022-04-20T00:35:55.493Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rodriguez2019},
 private_publication = {false},
 abstract = {Previous research studied a problem of data collection in complex networks with failure-prone components using mobile agents and two movement strategies: random and a pheromone-based algorithm. As a main conclusion, a fast data collection implies higher robustness and success rates. In some scale-free networks with a higher standard deviation in the betweenness centrality, random exploration was faster than a pheromone-based algorithm because mobile agents remain re-exploring nodes for more time. This paper presents an improvement to selected movement algorithms to collect data in complex networks in a faster way. The proposed improvement consists of local marks in nodes to avoid re-exploration combined with the previously proposed algorithms. Experiments were performed with different failures rates. Results show that there is a significant difference between the pheromone algorithm with and without local marks providing a higher robustness in data collection tasks in scenarios with a higher standard deviation in the betweenness centrality. Possible applications include data-collection and retrieval in distributed environments like Internet of Things environments (IoT) as well as farms, clusters and clouds.},
 bibtype = {article},
 author = {Rodríguez, Arles and Botina, Nathaly and Gómez, Jonatan and Diaconescu, Ada},
 editor = {Pinto, David and Singh, Vivek},
 doi = {10.3233/JIFS-179053},
 journal = {Journal of Intelligent and Fuzzy Systems},
 number = {5}
}

Previous research studied a problem of data collection in complex networks with failure-prone components using mobile agents and two movement strategies: random and a pheromone-based algorithm. As a main conclusion, a fast data collection implies higher robustness and success rates. In some scale-free networks with a higher standard deviation in the betweenness centrality, random exploration was faster than a pheromone-based algorithm because mobile agents remain re-exploring nodes for more time. This paper presents an improvement to selected movement algorithms to collect data in complex networks in a faster way. The proposed improvement consists of local marks in nodes to avoid re-exploration combined with the previously proposed algorithms. Experiments were performed with different failures rates. Results show that there is a significant difference between the pheromone algorithm with and without local marks providing a higher robustness in data collection tasks in scenarios with a higher standard deviation in the betweenness centrality. Possible applications include data-collection and retrieval in distributed environments like Internet of Things environments (IoT) as well as farms, clusters and clouds.

@phdthesis{
 title = {Termites system with self-healing based on autonomic computing},
 type = {phdthesis},
 year = {2011},
 keywords = {Auto-recuperación,Autonomic computing,Computación autonómica,Computación evolutiva / Multiagent systems,Evolutionary computing,Fallas en programas,Inteligencia de enjambres,Juegos de lenguaje,Language games,Program failures,Self-healing,Sistemas multiagente,Swarm intelligence,Termitas,Termites},
 websites = {http://www.bdigital.unal.edu.co/5414/},
 institution = {Universidad Nacional de Colombia},
 department = {Departamento de Sistemas y Computación},
 id = {db4bc8a6-18c1-37e4-be83-b578be9dd6d0},
 created = {2012-09-25T18:05:04.000Z},
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 last_modified = {2019-11-06T19:15:32.959Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rodriguez2011c},
 user_context = {Mg. Eng Thesis.},
 private_publication = {false},
 bibtype = {phdthesis},
 author = {Rodriguez, Arles and Gomez, Jonatan}
}