Random Field Estimation with Delay-Tolerant Wireless Sensor Networks: Quantize-and-Estimate vs. Compress-and-Estimate Encoding

In this paper, we study the problem of random field estimation with wireless sensor networks. To that aim, we adopt two encoding strategies, namely Compress-and-Estimate (CE) and Quantize-and-Estimate (QE), which operate with and without side information at the decoder, respectively. We focus our attention on delay-tolerant (DT) networks where sensors have the flexibility to encode and transmit a variable number of samples within a horizon of L consecutive timeslots. In this context, we derive a closed-form expression of the optimal number of samples to be encoded in each timeslot. Besides, we identify buffer stability conditions and a number of interesting distortion vs. buffer occupancy trade- offs. Computer simulation and numerical results are given in terms of distortion vs. number of sensor nodes or SNR, and buffer occupancy. Performance in delay-constrained (DC) scenarios is used as a benchmark.