Response driven efficient task load assignment in mobile crowdsourcing

Abstract

Mobile crowdsourcing paradigm is considered as one of the emerging techniques due to immense demand of location based services and various novel applications in recent years. The evolution of smart mobile users (SMUs), specifically due to high end mobile devices in terms of resources and capabilities has contributed towards the concept of collaborative task completion and a notion of crowdsourcing. Under general scenario of mobile crowdsourcing, an application based platform (task requester) tries to motivate a number of available participating users for completing a specific task by introducing certain incentive mechanism. However, the challenge remains in improving users' participation for a better result as not all users have similar attitude for a task due to resource constraints(energy profile), time, mobility, privacy issues and so on. Therefore, to address this situation, in this paper we propose users' response profile based incentive mechanism for improving participation that incorporates users' behavior and inconvenience metrics upon joining crowdsourcing. Secondly, we formulate utility based optimal task load allocation considering energy constraints of SMUs. Simulation results show response driven incentive mechanism supports platform owner to design an appropriate task load allocation scheme without overwhelming SMU's energy constraint and eventually loosing participation.