Spare the Mule, Help your Neighbors: Robot Route Planning for Data Retrieval on Large Scale Sensor Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013-10-17

Autores

D. E. Lucani (Autor) Outra; Sujit Baliyarasimhuni (Autor) FEUP; João Tasso Sousa (Autor) FEUP

Ata de Conferência Internacional

Título: 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

Páginas: 2534-2541

IEEE International Conference on Robotics and Automation (ICRA)

Karlsruhe, GERMANY, MAY 06-10, 2013

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-008-GCY

DOI: 10.1109/icra.2013.6630923

Abstract (EN): A fundamental knowledge of the trade-off between sensor cooperation and autonomous vehicles' (A V) trajectory planning is pivotal towards characterizing the sensing capabilities of wireless sensor networks that employ AV for collecting the data and to ensure their successful integration in large scale sensor deployments. We formulate the problem of efficient data gathering as a mixed integer linear programming (MILP) problem that provides a joint optimization of AV trajectory and data-routing. Since MILP formulations are not scalable, we propose an approach to develop heuristics where the joint optimization is decoupled into three sub-problems. The first is to determine clusters of sensors with communication range limitations. The second is to efficiently connect the clusters. The third is to design the route inside the cluster that will minimize the cost of data collection. We characterize performance of the proposed heuristics through Monte-Carlo simulations. Performance is measured in terms of (a) the joint energy cost for cooperation and AV movement for different number of sensor nodes and communication ranges of these sensors, and (b) computational effort of the various heuristics. For small deployments, we compare the heuristics to the MILP global optimization and show that the gap between them can be lower than 2% for deployments as large as 18 nodes and typically below 25 % for a wide range of scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8