IEEE Transactions on Information Theory
The C programming language
Communication complexity
Some complexity questions related to distributive computing(Preliminary Report)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
Collecting correlated information from a sensor network
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
IEEE Transactions on Mobile Computing
Coding for interactive communication
IEEE Transactions on Information Theory - Part 1
IEEE Transactions on Information Theory
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
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We consider the distributed function computation problem where the sink computes some function of the data split among N correlated informants, in asymmetric communication scenarios. The distributed function computation problem is addressed as a generalization of distributed source coding (DSC) problem. We are interested in computing the minimum number of informant bits required, in the worst-case, to allow the sink to exactly compute the function. We provide a constructive solution for this in terms of an interactive communication protocol and prove its optimality. The proposed protocol also allows us to compute the worst-case achievable rate-region for the computation of any function. We introduce two equivalence classes of functions: lossy and lossless and show that, in general, the lossy functions can be computed with fewer informant bits than the DSC problem, while computation of the lossless functions requires as many informant bits as the DSC problem.