Research on Industrial Things Internet MCS Ranking Task Recommendation in Iot Based on Collaborative Learning
In order to further improve the enthusiasm of Mobile Crowd Sensing(MC)participants in the Internet of Things,combined with the advantages of Hybrid model(HM)and List-Wise Ranking(LWR)algorithms,An HM-LWR collaborative learning method is designed and MCS ranking task recommendation analysis is carried out.The results show that compared with non-optimized LWR,HM-LWR collaborative learning method has good growth rate and ideal processing performance.HM-LWR collaborative learning method has the highest enthusiasm of participants,with a participation rate of about 98％.It can obtain high assignment accuracy,reduce the time required for task assignment,and obtain higher task execution efficiency,which has significant advantages over LWR algorithm.This research is helpful to improve the operation effect of industrial Internet of Things and has a good energy-saving effect.
Internet of Thingsmobile swarm intelligence perceptioncollaborative learninghybrid modelparticipant intention
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