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We develop cloud-assisted remote sensing techniques for enabling distributedconsensus estimation of unknown parameters in a given geographic area. We firstpropose a distributed sensor network virtualization algorithm that searchesfor, selects, and coordinates Internet-accessible sensors to perform a sensingtask in a specific region. The algorithm converges in linearithmic time forlarge-scale networks, and requires exchanging a number of messages that is atmost linear in the number of sensors. Second, we design an uncoordinated,distributed algorithm that relies on the selected sensors to estimate a set ofparameters without requiring synchronization among the sensors. Our simulationresults show that the proposed algorithm, when compared to conventional ADMM(Alternating Direction Method of Multipliers), reduces communication overheadsignificantly without compromising the estimation error. In addition, theconvergence time, though increases slightly, is still linear as in the case ofconventional ADMM.

Cloud-Assisted Remote Sensor Network Virtualization for Distributed Consensus Estimation