Towards Cost-Effective and Budget-Balanced Task Allocation in Crowdsourcing Systems

Crowdsourcing has been considered as one of the most promising services in recent years. More and more crowdsourcing platforms allocate tasks over the social network due to its pervasiveness. Although most research focuses on direct contribution-based task allocation with some budget constraints, a robust task allocation scheme should also consider the task allocation in the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an NP-Complete problem, cost-effective and budget-balanced task allocation (CBTA) problem, specially for the WoM mode crowdsourcing over social network, which aims to minimize the overall budget consumption as well as balance the budgets among target social groups. Furthermore, we propose two heuristic algorithms CB-greedy and CB-local based on greedy strategy and local search technique respectively to construct a spanning tree for task allocation. We prove that the running time of CB-greedy is O (m(2) log m) while CB-local utilizing disjoint-set achieves O (mn alpha (m; n)), where m is the number of edges indicating interactions of social groups, n is the number of social groups, and alpha is the inverse Ackerman function. Extensive simulations show that the proposed algorithms guarantee the criteria to a large extent. To the best of our knowledge, it is the first work jointly optimizing cost effectiveness and budget balance in the WoM mode crowdsourcing systems.