Source coding in the presence of exploration-exploitation tradeoff

Exploration versus exploitation in a sensor field with a mobile agent is examined in the context of source coding. The encoder is the low complexity data gathering agent. The decoder is a high complexity fusion center. The encoder first sends a coarse description of the random field, then transmits a refined description of a region of interest, i.e., a subset of the correlated sources in the first stage and so on. The main source coding challenge is that the receiver wants to refine a subset of the correlated sources that is unknown to the encoder a priori. The conventional approach of scalable coding via conditional codebook encoding (CCE) requires a codebook that is exponential in size with respect to the number of sources and also the number of refinement stages. This paper studies an alternative approach, using random binning (RB), in lieu of CCE. The universality of RB plays a key role, as the encoder does not know a priori which sources the decoder wants to refine. It is shown that RB does not introduce any loss and can effectively replace CCE while providing significant storage reduction in terms of the number of codewords stored. Achievable rate regions are derived for the single and the multi-terminal encoding settings.