An IoT Service Aggregation Method Based on Dynamic Planning for QoE Restraints
With the rapid development of new internet technologies, i.e. Internet of Things (IoT), the amount of IoT services has grown dramatically. To make people easier and faster to utilize the service resources, it becomes more and more important to gather the services on the IoT. Most of the current methods can only create automatic or semi-automatic service composition schemes, and lack support and consideration of real-time data and instant situation information, so they cannot achieve dynamic adaptive aggregation of services. In this paper, we focus on both the functional and non-functional constrains, and propose an IoT service aggregation method based on dynamic planning oriented QoE constraint. Firstly, the knowledge model of relationship among service category concepts are constructed. Secondly, the aggregation problem of service categories is mapped to a dynamic programming problem based on the relationship between service composition, and a new semantic similarity computing method is used as the main basis for service selection by using the ontology of IoT service category. Finally, for the selection of specific service resources, a trend-aware service selection algorithm for the QoE multi-constrained measurement is proposed. Experimental results show that the proposed method has better performance in terms of recall and precision.
KeywordsInternet of things Dynamic programming Service selection Service aggregation QoE
This work is supported by the National Natural Science Foundation of China under Grants 41761086, 61461037, 61761035 and 61661041, the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant No.2016YFB0502102, the Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2017JQ09, and the “Grassland Elite” Project of the Inner Mongolia Autonomous Region under Grant CYYC5016.
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