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Abstract: The energy scaling laws of multihop data fusion networks for distributedinference are considered. The fusion network consists of randomly locatedsensors distributed i.i.d. according to a general spatial distribution in anexpanding region. Among the class of data fusion schemes that enable optimalinference at the fusion center for Markov random field MRF hypotheses, thescheme with minimum average energy consumption is bounded below by averageenergy of fusion along the minimum spanning tree, and above by a suboptimalscheme, referred to as Data Fusion for Markov Random Fields DFMRF. Scalinglaws are derived for the optimal and suboptimal fusion policies. It is shownthat the average asymptotic energy of the DFMRF scheme is finite for a class ofMRF models.



Author: Animashree Anandkumar, Joseph E. Yukich, Lang Tong, Ananthram Swami

Source: https://arxiv.org/







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