Smart devices around us produce a considerable volume of data and interact in a wide range of scenarios that guide the evolution of the Internet of Things (IoT). IoT adds informative and interactive aspects to our living spaces, converting them into smart spaces. However, the development of applications is challenged by the fragmented nature due to the vast number of different IoT things, the format of reported information, communication standards, and the techniques used to design applications. This paper introduces IoT Hub as a Service (HaaS), a data-oriented framework to enable communication interoperability between the ecosystem’s entities. The framework abstracts things' information, reported data items, and developers' applications into programmable objects referred to as Cards. Cards represent specific entities and interactions of focus with meta-data. The framework then indexes cards’ meta-data to enable interoperability, data management, and application development. The framework allows users to create virtual smart spaces (VSS) to define cards' accessibility and visibility. Within VSS, users can identify accessible data items, things to communicate, and authorized applications. The framework, in this paper, defines four types of Cards to represent: participating IoT things, data items, VSS, and applications. The proposed framework enables the development of synchronous and asynchronous applications. The framework dynamically creates, updates, and links the cards throughout the life-cycle of the different entities. We present the details of the proposed framework and show how our framework is advantageous and applicable.
The applications of Internet-of-things ( IoT ) deploy massive number of sensors to monitor the system and environment. Anomaly detection on streaming sensor data is an important task for IoT maintenance and operation. In real IoT applications, many sensors report categorical values rather than numerical readings. Unfortunately, most existing anomaly detection methods are designed only for numerical sensor data. They cannot be used to monitor the categorical sensor data. In this study, we design and develop a 3D Histogram based Categorical Anomaly Detection (HCAD) solution to monitor categorical sensor data in IoT. HCAD constructs the histogram model by three dimensions: categorical value, event duration, and frequency. The histogram models are used to profile normal working states of IoT devices. HCAD automatically determines the range of normal data and anomaly threshold. It only requires very limit parameter setting and can be applied to a wide variety of different IoT devices. We implement HCAD and integrate it into an online monitoring system. We test the proposed solution on real IoT datasets such as telemetry data from satellite sensors, air quality data from chemical sensors, and transportation data from traffic sensors. The results of extensive experiments show that HCAD achieves higher detecting accuracy and efficiency than state-of-the-art methods.
A BSTRACT Datacubes form an accepted cornerstone for analysis-and visualization-ready spatio-temporal data offerings. The increase in user friendliness is achieved by abstracting away from the zillions of files in provider-specific organization. Datacube query languages additionally establish actionable datacubes, enabling users to ask "any query, any time" with zero coding. However, typically datacube deployments are aiming at large scale, data center environments accommodating Big Data and massive parallel processing capabilities for achieving decent performance. In this contribution, we conversely report about a downscaling experiment. In the ORBiDANSE project a datacube engine, rasdaman, has been ported to a cubesat, ESA OPS-SAT, and is operational in space. Effectively, the satellite thereby becomes a datacube service offering the standards-based query capabilities of the OGC Web Coverage Processing (WCPS) geo datacube analytics language. We believe this will pave the way for on-board ad-hoc processing and filtering on Big EO Data, thereby unleashing them to a larger audience and in substantially shorter time.
A smart manufacturing line consists of multiple connected machines. These machines communicate with each other over a network, to solve a common task. Such a scenario can be located in the Internet of Things (IoT) area. An individual machine can be perceived as an IoT device. Due to machine to machine communication, a huge amount of data is generated during manufacturing. This emerging data flow is an essential part of today’s industry, as analyzing data helps improving processes and thus, product quality. To adequately make use of the collected data, we require a high level of data quality. In our work, we address the issue of inconsistent data in smart manufacturing and present an approach to automatically generate SPARQL queries for validation.
and the integration of ML models to enable predictions on the patients’ health state. Using our end-to-end solution, we implement a real-world patient monitoring scenario for hemodynamic and pulmonary decompensations, which are dynamic and life-threatening deteriorations of lung and cardiovascular functions. Our application provides ad-hoc views of the vital parameters and derived decompensation severity scores with continuous updates on the latest data readings to support timely decision-making by physicians. Furthermore, we envision the infrastructure of an IoT ecosystem for a multi-hospital scenario that enables geo-distributed medical participants to contribute data to the application in a secure, private, and timely manner.
