QoI-Aware Energy-Efficient Participatory Crowdsourcing

Today's smartphones not only serve as a means of personal communication device, but are also fundamentally transforming the traditional understanding of crowdsourcing to an emerging type of participatory, task-oriented applications. It aims to support the so-called Citizen Science efforts for knowledge discovery, to understand the human behavior and measure/evaluate their opinions. In this paper, to facilitate the above scenarios, we propose a novel energy-efficient participatory crowdsourcing framework that meets the quality-of-information (QoI) requirements of the request in a distributed manner. Specifically, we extend the traditional framework of Gur Game for distributed decision-making to recommend the level of information contribution for each participant, by merging the multiple automaton chains into a single chain with multiple steady states. We evaluate the proposed scheme under the MIT social evolution data set, where the QoI requirements of the request are successfully achieved, with a satisfactory level of energy consumption fairness among participants, of negligible computational complexity. Finally, we explore the impact of community structure on the proposed algorithm, and propose a feasible method to facilitate the local data aggregation.