We 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, we analyse encounter traces to place an upper bound on the amount of computation that is possible in such networks. We also investigate a practical task-farming algorithm that approaches this upper bound, and show that exploiting social structure can dramatically increase its performance.
The idea of crowd computing was first introduced by Murray et al. REF , which aims to combine mobile devices and social interactions to achieve large-scale distributed computation.
The case for crowd computing
MobiHeld '10
Computer Science