61 resultados para imaged-based control scheme
Filtro por publicador
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- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (2)
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- Galway Mayo Institute of Technology, Ireland (1)
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- Instituto Politécnico de Bragança (2)
- Instituto Politécnico do Porto, Portugal (45)
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- Martin Luther Universitat Halle Wittenberg, Germany (6)
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- Scielo Saúde Pública - SP (25)
- Universidad Autónoma de Nuevo León, Mexico (1)
- Universidad de Alicante (12)
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- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade do Minho (4)
- Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP) (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (10)
- Universitat de Girona, Spain (16)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (4)
- Université de Lausanne, Switzerland (19)
- Université de Montréal, Canada (4)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (3)
- University of Queensland eSpace - Australia (80)
- University of Washington (2)
Resumo:
Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.