Information fusion in wireless sensor networks with source correlation

In this paper, we consider a central estimating officer (CEO) scenario, where sensors observe a noisy version of a binary sequence generated by a single source (the “phenomenon”) and the access point (AP)’s goal is to estimate, by properly fusing the received data, this sequence. Due to this system model, the data sent by the sensors are correlated and, therefore, it is possible to exploit a proper a priori information in the localized fusion operation performed at the AP. In the presence of channel coding at the sensors and block faded communication links, we first derive the optimum maximum a priori probability (MAP) joint decoding and fusion rule, showing its computational unfeasibility. We then derive two suboptimal decoding/fusion strategies. In the first case, the fusion rule exploits the source correlation and receives, at its input, the soft-output values generated by a joint channel decoder (JCD). Two possible iterative JCD algorithms are proposed: one with “circular” iterations between the component decoders (associated with the sources) and one with “parallel” iterations between the component decoders. For each algorithm, two information combining strategies are considered. In the second case, a separate channel decoding (SCD) scheme is considered and the correlation is exploited only during the fusion operation. Our results show that the scheme with SCD followed by fusion basically leads to the same probability of decision error of the scheme with JCD and fusion with, however, a much lower computational complexity, thus making it suitable to resource-constrained scenarios.