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The authors introduce and motivate crowd computing, which combines mobile devices and social interactions to achieve large-scale distributed computation. An opportunistic network of mobile devices offers substantial aggregate bandwidth and processing power. In this paper, they analyse encounter traces to place an upper bound on the amount of computation that is possible in such networks. They also investigate a practical task-farming algorithm that approaches this upper bound, and show that exploiting social structure can dramatically increase its performance.
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