Data-Driven Many-Objective Crowd Worker Selection for Mobile Crowdsourcing in Industrial IoT

With the development of mobile networks and intelligent equipment, as a new intelligent data sensing paradigm in large-scale sensor applications such as the industrial Internet of Things, mobile crowd sensing (MCS) assigns industrial sensing tasks to workers for data collection and sharing, which has created a bright future for building a strong industrial system and improving industrial services. How to design an effective worker selection mechanism to maximize the utility of crowdsourcing is the research hotspot of mobile sensing technologies. This article studies the problem of least workers selection to make large MCS system perform sensing tasks more effective and achieve certain coverage with certain constraints being meeting. A many-objective worker selection method is proposed to achieve the desired tradeoff and an optimization mechanism is designed based on the enhanced differential evolution algorithm to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through a large scale of experimental evaluation datasets collected from real world.