Multi-objective task assignment for maximizing social welfare in spatio-temporal crowdsourcing

With the development of the Internet of Things (IoT), spatio-temporal crowdsourcing (mobile crowdsourcing) has become an emerging paradigm for addressing location-based sensing tasks. However, the delay caused by network transmission has led to low data processing efficiency. Fortunately, edge computing can solve this problem, effectively reduce the delay of data transmission, and improve data processing capacity, so that the crowdsourcing platform can make better decisions faster. Therefore, this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment (MOO-TA) problem in the edge computing environment. The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area. In this paper, the Weighted and Multi-Objective Particle Swarm Combination (WAMOPSC) algorithm is proposed to maximize both platform's and crowd workers' utility, so as to maximize social welfare. The algorithm combines the traditional Linear Weighted Summation (LWS) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose. Through comparison experiments on real data sets, the effectiveness and feasibility of the proposed method are evaluated.