Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain

Abstract

This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

“…The emerging paradigm of Internet of Things (IoT) is specially focused on collecting and processing data everywhere and all the time [ 4 , 5 , 6 , 7 ]. This expansion of the IoT is allowing to use all type of objects beyond their basic functions, or at least, for those they were designed for a wide range of applications.…”

An Associative Memory Approach to Healthcare Monitoring and Decision Making

“…Two decades ago, technology researchers took an important role towards improving the medical care of patients through the evolution of the concept of a network of smart devices, which would be known as Wireless Sensor Networks (WSNs) [ 4 ]. In the same decade, the concept of moving small amounts of data to a large set of nodes evolved to what today is known as the Internet of Things (IoT) [ 5 , 6 ]. The IoT paradigm represents one of the most disruptive technologies, enabling ubiquitous computing scenarios for medical monitoring, and decision making [ 7 , 8 ]; creating the well-known healthcare paradigm of e-Health [ 9 ].…”

A Hydrological Sensor Web Ontology Based on the SSN Ontology: A Case Study for a Flood

“…For example, Llaves et al [15] designed adapters for different data formats and distributed processing of streams in a cluster to reduce the average latency of message processing for Sensor Data Management in the Environmental Domain; Dutta and Morshed [16] proposed a domain ontology-based linked data approach to assess the reliability of the hydrological sensor network and evaluate the performance of the sensor network; Ploennigs et al [17] extended the SSN for automating the creation and configuration of physical models to detect and diagnose abnormal building behavior; Dey et al [18] described the organization of general sensor information and its management and particularly elaborated on the case for energy sensor using SSN ontology; Fernandez and Ito [19] used SSN ontology to manage sensor information in an intelligent transportation architecture, which performed the automatic traffic light settings allowing the prediction and avoidance of traffic accidents, and the routing optimization.…”

“…Related studies have only focused on hydrological data filtering and validation [15]; little attention has been paid to the dynamic processes of hydrological disasters, for instance, floods. Moreover, as the SSN ontology does not describe domain concepts, time, and locations.…”