Task trading for crowdsourcing in opportunistic mobile social networks
Xiao Chen · 2018
With the explosive proliferation of mobile devices, mobile crowdsourcing has become a new paradigm involving a crowd of mobile users to collectively take large-scale tasks from requesters in mobile social networks (MSNs). In this paper, we study task allocation in crowdsourcing in Opportunistic Mobile Social Networks (OMSNs) which are formed opportunistically when people gather together at social events. Specifically, we aim to minimize the total working hours of the users to finish these tasks. Different from other algorithms, we hope to raise the efficiency of the whole network by task trading inspired by the comparative advantage in macroeconomy. We first prove that our defined problem is NP-hard and then propose a heuristic task trading algorithm TTA by which users can trade when they meet opportunistically. Simulation results comparing our proposed algorithm with the one without considering trading and the brute force algorithm to find the minimum total number of hours show that our proposed algorithm can substantially reduce the total number of hours to finish all the allocated tasks and is very close to the benchmark brute force algorithm.