Scalable Source/Channel Decoding for Large-Scale Sensor Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2004

Autores

João Barros (Autor) FEUP; M. Tuechler (Autor) FEUP; S. P. Lee (Autor) Outra

Ata de Conferência Internacional

Título: IEEE International Conference in Communications (ICC2004)

Páginas: 881-885

In Proc. of the IEEE International Conference in Communications (ICC2004)

Paris , June 2004

Indexação

ISI Web of Science

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

DOI: 10.1109/ICC.2004.1312628

Abstract (EN): We consider the sensor reachback problem, in which a large number of sensor nodes are deployed on a field, and the goal is to reconstruct at a remote location the correlated data collected and transmitted by all the nodes. In this paper, we assume that each sensor node uses a very simple encoder (a scalar quantizer and a modulator) and focus on decoding algorithms that exploit the correlation structure of the sensor data to produce the best possible estimates under the minimum mean square error (MMSE) criterion. Our analysis shows that the optimal MMSE decoder is unfeasible for large scale sensor networks, because its complexity grows exponentially with the number of nodes in the network. Seeking a scalable alternative, we use factor graphs to obtain a simplified model for the correlation structure of the sensor data. This model allows us to use an iterative decoding algorithm whose complexity can be made to grow linearly with the size of the network.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5