Affiliations: [a] Fundación Universitaria Konrad Lorenz, ALIFE Research Group Universidad Nacional de Colombia, Bogotá, Colombia
| [b] Fundación Universitaria Konrad Lorenz, Bogotá, Colombia
| [c]
ALIFE Research Group, Universidad Nacional de Colombia, Bogotá, Colombia
| [d] Telecom ParisTech, LTCI, IMT, Paris, France
Correspondence:
[*]
Corresponding author. Arles Rodríguez, Fundación Universitaria Konrad Lorenz, ALIFE Research Group Universidad Nacional de Colombia, Carrera 10 No 64-61, 6 Piso, Bogotá, Colombia. E-mail: [email protected].
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.
Keywords: Data collection, complex networks, failure-prone mobile agents, local marking
