【摘 要】
：
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.How-ever,the delay caused by network transmission has led to low data pro
【机 构】
：
School of Computer and Control Engineering,Yantai University,Yantai 264005,China
论文部分内容阅读
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.How-ever,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 Op-timization Task Assignment (MOO-TA) problem in the edge computing environment.The proposed on-line incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sens-ing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Com-bination (WAMOPSC) algorithm is proposed to maxi-mize 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 Opti-mization (MOPSO) algorithm to find pareto optimal solutions of multi-objective optimization task assign-ment 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.
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