.png){kind=icon}
返回
Geospatial complex event processing in smart city applications
.png){kind=icon}
OA
AI
DOI：10.1016/j.simpat.2022.102675.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The extensive deployment of the Internet of things (IoT) devices in urban areas enables smart city systems (e.g., urban traffic detection and air pollution monitoring) to use these devices to discover the affected regions by various events, particularly complex ones. A complex event is inferred from patterns of different individual primitive events according to their spatio-temporal information. Complex event processing (CEP) systems facilitate applications to define their reasoning rules based on event processing languages (EPL), similar to SQL. However, current languages and CEP engines do not efficiently support spatial data characteristics. Geospatial CEP is challenging since applications have diverse definitions for complex events (e.g., air quality index), IoT devices have heterogeneous specifications/capabilities, and complex events may happen concurrently. This paper proposes the Geo-Tesla language that enables smart city applications to define spatial attributes in complex event definitions and reasoning rules. Then, we devise the GeoT-Rex CEP engine that implements geospatial operations that compiles Geo-Tesla language and processes the spatial data. The integration of Geo-Tesla and GeoT-Rex enables applications to process complex events with spatial characteristics in their reasoning rules and identify boundaries of the detected complex event (event footprint). The evaluation results from the proof of concept implementation show that our proposed solution outperforms in identifying the footprint of complex events up to 44% in the largest network size. They also show that the detection percentage in the concurrent appearance of complex events is 40% more on average than a close state-of-the-art baseline.
Keyword:
Complex event processing
Spatio-temporal stream processing
T-Rex engine
TESLA language
Edge computing
Smart city
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
2.6K
被引数:
4.8K
机构
引用论文
Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform†.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏bIoTope: Building an IoT Open Innovation Ecosystem for Smart CitiesbIoTope: 为智慧城市构建物联网开放创新生态系统IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏A further characterization of alpha-2 adrenoceptor involvement in the rough-and-tumble play of juvenile rats.Α-2肾上腺素受体参与幼鼠粗暴运动的进一步表征。
IF0
PREAI
分享收藏
查看更多内容