A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing

Abstract

Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

“…The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. The present experimental research is a continuation of our previous work [5], which has been extended with an adaptive data aggregation approach in real-time stream processing.…”

“…Enabling smart cities to efficiently manage traffic and parking data and provide alternative routes will not only help in reducing transportation cost but also pollution that has been caused by traffic congestion. As a use case scenario, we use public traffic 5 and parking data 6 that has been obtained from the city of Aarhus in Denmark. Our experimental dataset consists of two sets of stream samples: (i) traffic sensor observations (i.e.…”

IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

“…They are planned to be used by other independent platforms in the open calls of the H2020 project FIESTA-IoT 11 (aspects 3 and 5). We plan to develop annotation and validation tools for IoT-Lite, by extending our SAOPY annotation tool 12 [16] and the SSN validator tool 13 (aspect 4).…”

“…IoT-Lite can be a core part of a semantic model in which, depending on the applications, different semantic modules can be added to provide additional domain and application specific concepts and relationships. In this sense we have linked IoT-Lite to Stream Annotation Ontology (SAO) [16], in order to allow the annotation of aggregated data streams, which follows 1 http://www.foaf-project.org/ the philosophy of IoT-Lite in the sense of lightweight ontology and fast response time to queries.…”

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

“…In analysis of the time-series the similarity can be computed by comparing the observed values and can be computed also using meta information such as time or type. After features are extracted from sensors data we need to classify these features and there are many techniques [30] developed for this purpose as show in the figure below.…”