Optimized Data Fusion in Bandwidth and Energy Constrained Sensor Networks

This paper considers the problem of decentralized data fusion (DDF) for large wireless sensor networks with stringent bandwidth requirements. To reduce the power and bandwidth costs of wireless transmissions, each sensor node is confined to quantize its sensing data and send 1-bit information only. Under this setting, we derive the maximum likelihood (ML) data fusion rule for decentralized parameter estimation, and analyze its Cramer-Rao lower bound (CRLB) of the fusion performance in the sense of mean square distortion. Depending on the underlying noise characteristics, our 1-bit DDF scheme can achieve estimation performance competitive to or even surprisingly better than that of centralized fusion over unquantized data. There is considerable saving in communication costs, which in turn reduces network energy consumption. Furthermore, we investigate network optimization, for which a worst-case robust design methodology is adopted to formulate a well-behaved min/max optimization problem. From the information processing viewpoint, the resulting optimized network offers robust fusion performance at minimal costs of communication resources