An Integrated Framework for Data Quality Fusion in Embedded Sensor Systems

The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator.

Semantic Description of Quality of Data in Sensor Networks

The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.

Data Quality Management in the Internet of Things

Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.

A Low Energy IoT Application Using Beacon for Indoor Localization

In recent years, a great number of applications in Internet of Things (IoT) have been developed. In this context, many methodologies and technologies are used for several frameworks, including indoor localization. In this field, as highlighted in recent years, one of the most important aims of modern indoor applications in IoT is to identify how to manage and correctly convey people flow. This study aims to investigate the most common methods, technologies and applications for indoor localization in IoT and analyze the major systems currently in use and the application of these solutions to actual conditions. Following this, we propose an innovative method to detect people flow in indoor location. Based on Bluetooth low energy (BLE) technology, in this paper we analyze the possibility to use our system for many kinds of applications. The first experimental results show good performance of our system.

Knowledge-Based Smart City Service System

A smart city can be defined as a city exploiting information and communication technologies to enhance the quality of life of its citizens by providing them with improved services while ensuring a conscious use of the available limited resources. This paper introduces a conceptual framework for the smart city, namely, the Smart City Service System. The framework proposes a vision of the smart city as a service system according to the principles of the Service-Dominant Logic and the service science theories. The rationale is that the services offered within the city can be improved and optimized via the exploitation of information shared by the citizens. The Smart City Service System is implemented as an ontology-based system that supports the decision-making processes at the government level through reasoning and inference processes, providing the decision-makers with a common operational picture of what is happening in the city. A case study related to the local public transportation service is proposed to demonstrate the feasibility and validity of the framework. An experimental evaluation using the Situation Awareness Global Assessment Technique (SAGAT) has been performed to measure the impact of the framework on the decision-makers’ level of situation awareness.

Data Imputation in Wireless Sensor Networks Using a Machine Learning-Based Virtual Sensor

Data integrity in wireless sensor networks (WSN) is very important because incorrect or missing values could result in the system making suboptimal or catastrophic decisions. Data imputation allows for a system to counteract the effect of data loss by substituting faulty or missing sensor values with system-defined virtual values. This paper proposes a virtual sensor system that uses multi-layer perceptrons (MLP) to impute sensor values in a WSN. The MLP was trained using a genetic algorithm which efficiently reached an optimal solution for each sensor node. The system was able to successfully identify and replace physical sensor nodes that were disconnected from the network with corresponding virtual sensors. The virtual sensors imputed values with very high accuracies when compared to the physical sensor values.