Exploiting the sensing relevancies of source nodes for optimizations in visual sensor networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Costa, DG (Autor) FEUP; Guedes, LA (Autor) Outra

Revista

Título: Multimedia Tools and Applications

Vol. 64 Nº 3

Páginas: 549-579

ISSN: 1380-7501

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 24 Citações

Scopus - 31 Citações

Outras Informações

ID Authenticus: P-00W-KR8

DOI: 10.1007/s11042-011-0961-4

Abstract (EN): Wireless ad-hoc networks composed of resource-constrained camera-enabled sensors can provide visual information for a series of monitoring applications, enriching the understanding of the physical world. In many cases, source nodes may have different sensing relevancies for the monitoring functions of the applications, according to the importance of the visual information retrieved from the monitored field. As a direct result, high quality is only required for the most relevant information and, as it is expected that many visual monitoring applications can tolerate some quality loss in the data received from the least relevant source nodes, the network operation can be optimized exploiting this innovative concept. As a novel global QoS parameter, we envisage that the sensing relevancies of source nodes can be considered for a series of optimizations in different aspects of the wireless sensor network operation, achieving energy saving or assuring high quality transmission for the most relevant data. In this paper we discuss some approaches for the establishment of the sensing relevancies of the nodes and propose a protocol to support them. Moreover, we present two practical examples of optimizations based on the sensing relevancies of source nodes that transmit still images of the monitored field, addressing issues as energy-efficient data transmission and packet prioritization in intermediate nodes. © 2011 Springer Science+Business Media, LLC.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 31