A distributed semantic filtering model based on approximate automata for heterogeneous multi-sensor networks. (2016)

• Record Type: Journal Article
• Authors: Wang, Tong; Tao, Xueling; Zhou, Yucai; Li, Pengcheng; Zhao, Chunhui
• Is Part Of: International journal of sensor networks. Volume 20:Number 1(2016)
• Page Start: 46
• Page End: 53
• Publication Date: 2016
• Subjects: publish-subscribe systems -- multi-sensor networks -- data integration -- heterogeneous monitoring data -- XML -- middleware -- distributed filtering -- semantic filtering -- approximate automata -- sensor networks -- semantic coupling -- speed matching -- simulation -- internet of things -- IOT; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

Abstract

An increasing number of sensor networks are deployed to collect information on a wide range of applications. However, these sensor networks are isolated to each other. Meanwhile, processing large-scale of data stream is the neck of the push-mode system. To match the huge event stream in heterogeneous sensor network data, we propose approximation semantic factor (ASR), according to which we further propose the approximately semantic matching model for heterogeneous multi-sensor networks called approximately semantic matching model for heterogeneous multi-sensor networks (ASMMN). Furthermore, the evaluate pattern algorithm is put forward. ASMMN can effectively solve the semantic coupling and speed matching. Simulation results show that the proposed model can accelerate the matching process and reduce memory consumption of intermediate states among event streams. Meanwhile, the model has a good scalability that can be widely used in the heterogeneous push-mode sensor networks and internet of things (IOTs).