.png){kind=icon}
.png){kind=icon}
Return
A Framework for IoT Streaming Data Indexing and Query Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2022.3149901.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Streaming data are continuously generated by multiple Internet of Things (IoT) sources, e.g., sensors, mobile devices, etc., and sent simultaneously to relevant applications to be processed in real time in a continuous and timely fashion. Existing research has dealt with the integration of IoT streaming data from multiple sources. Some of the earlier research introduced models to facilitate streaming data compression and the subsequent indexing. However, as IoT data come from multiple sensors and differ in terms of variety and velocity, there is still an urgent need to build an efficient indexing mechanism so that we can optimise responses to users' queries. In this research, we identify a variety of queries from different motivating scenarios and develop a framework to optimise the way to access and retrieve IoT streaming data to respond to the users' queries. To this end, (i) a streaming data scenario is analysed, and (ii) an optimisation framework with indexing schemes from multiple sources are introduced, and (iii) some common and illustrative queries are presented to explain optimisation. Finally, a set of experiments on five queries is performed to illustrate the ability of the optimisation framework in a wide range of different scenarios. The results prove that the proposed optimisation framework is much better than the existing IoT data integration and indexing frameworks.
Keywords:
Indexing
Optimization
Data models
Real-time systems
Internet of Things
Hidden Markov models
Data integration
Internet of Things
data integration
data indexing
data compression
query optimization