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About 10,000 papers for “sensor data integration”

Data integration: Sensor networks:

Lyublena Antova, Db Db, John John · 2009

(1) single (2) married (3) divorced (4) widowed Smith’s SSN? Brown’s marital status? How to make sure SSN is unique? Social Security Number:

Cited by 0

Sensor integration and data fusion

Stelios C. A. Thomopoulos · Journal of Robotic Systems · 1990

Abstract The problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that information from various sensors may be available in different forms at the fusion. For example, data from infrared (IR) sensors may be combined with range radar (RR) data and further combined with visual images. In each case, data and information from different sensors are presented in a different format which may not be directly compatible for all sensors. Part of the available information may be in the form of attributes and part in the form of dynamical measurements. A generalized evidence processing theory and an architecture for sensor integration and data fusion that accommodates diversified sources of information are presented. Data (or, more generically, information) fusion may take place at different levels, such as the level of dynamics, the level of attributes, and the level of evidence. The common and different aspects of fusion at the different levels are investigated and several practical examples of real world data fusion problems are discussed.

Cited by 123

Microservice approach of sensor data integration

Bock, Nikolai (Hochschule Mainz-University Of Applied Sciences) · TIB KMO / FLOWWORKS GmbH · 2016

The flexible integration of sensor data from several sources with different concepts is a interesting current research topic. This talk will show a solution which is developed as part of the “ActOnAir” project. The aim of “ActOnAir” is the development of a mobile recommendation system based on personalized environment sensor information in the e-health section. The talk focuses the sensor integration part. The system receives information from the following sources: mobile sensor devices (for particulate matter, ozone, etc.); activity trackers; human sensor information from smartphone apps. Additionally different online services for air quality data or weather information will be merged. Social Media information from concepts like “Collective Sensing” are also possible. Considering the interoperability of the sensor information the persistence of the information for data mining and visualization will be based on Sensor Web Enablement (SWE) standards. In respect to the flexibility and scalability, the presented approach will be based on microservices which provide the different sensor sources, processing steps for integration as well as sinks (such as a Sensor Observation Service (SOS)). The integration process can be defined in data streams using the provided services as modules. Also tapping into existing streams is possible. The deployment on different platforms will be easily supported, because of the microservice approach. During the talk the overall integration system concept, as well as the development of central modules, will be shown.

Cited by 0Open access

Sensor Integration and Data Fusion Theory

Zude Zhou, Huaiqing Wang, Ping Lou · Advances in civil and industrial engineering book series · 2010

In previous chapters, the engineering scientific foundations of manufacturing intelligence (such as the knowledge-based system, Multi-Agent system, data mining and knowledge discovery, and computing intelligence) have been discussed in detail. Sensor integration and data fusion is another important theory of manufacturing intelligence. With the development of integrated systems, there is an urgent requirement for improving system automaticity and intelligence. Without improvement, the complexity and scale of systems are increased. Such systems need to be more sensitive to their work environment and independent state, and obviously, single sensor technology hardly meets these requirements. Multi-sensor and data fusion technology are therefore employed in automatic and intelligent manufacturing as it is more comprehensive and accurate than traditional single sensor technology if the information redundancy and complementarity are used reasonably. In theory, the outputs of multi-sensors are mutually validated. Multi-sensor integration is a brand new concept for intelligent manufacturing, and without doubt, sensor integration-based intelligent manufacturing is the development orientation of manufacturing in the future. With reference to the information fusion problem of the multi-sensor integration system, the development state, technical background, application scope and basic meaning of the multi-sensor integration and the data fusion are first reviewed in this chapter. Secondly the classification, level, system structure and function model of the data fusion system is discussed. The theoretical method of the data fusion is then introduced, and finally, attention is paid to cutting tool condition detection, machine thermal error compensation and online detection and error compensation because those are the main applications of multi-sensor data fusion technology in intelligent manufacturing.

Cited by 0

Integration of Reliable Sensor Data Stream . . .

Gert Brettlecker, Heiko Schuldt, Peter Fischer et al. · 2008

Data Stream Management (DSM) addresses the continuous processing of sensor data. DSM requires the combination of stream operators, which may run on different distributed devices, into stream processes. Due to the recent advantages in sensor technologies and wireless communication, the amount of information generated by DSM will increase significantly. In order to efficiently deal with this streaming information, Digital Library (DL) systems have to merge with DSM systems. Especially in healthcare, the continuous monitoring of patients at home (telemonitoring) will generate a significant amount of information stored in an e-health digital library (electronic patient record). In order to stream-enable DL systems, we present an integrated data stream management and Digital Library infrastructure in this work. A vital requirement for healthcare applications is however that this infrastructure provides a high degree of reliability. In this paper, we present novel approaches to reliable DSM within a DL infrastructure. In particular, we propose information filtering operators, a declarative query engine called MXQuery, and efficient operator checkpointing to maintain high result quality of DSM. Furthermore, we present a demonstrator implementation of the integrated DSM and DL infrastructure, called OSIRIS-SE. OSIRIS-SE supports flexible and efficient failure handling to ensures complete and consistent continuous data stream processing and execution of DL processes even in the case of multiple failures.

Cited by 0

Ultrasonic Sensor Data integration and its Appiicatiun

Envimmnt Perception, Charles C. Chang · 1994

To move in an unknown or uncertain environment, a mobile robot must collect informa-tion from various sensors and use it to construct a representation of the external world. Ultrasonic sensors can provide range data for this purpose in a simple and cost-effective way. However, most ultrasonic sensors are not sufficient for environment recognition because of their large beam opening angles. In this article the beam-opening-angle problem is solved by fusing data from multiple ultrasonic sensors. We propose two methods for sensor data fusion. One uses an artificial neural network (ANN), and the other is based on a mathematical model. Simulations and experiments show that the mathematical model is more accurate when there is no noise in the sensor readings, but the ANN method is better when the sensors are subject to much noise. To extract line segments from the ultrasonic image, we develop a line extractor that is more efficient than traditional line fitting methods in this application. Experimental results show that this method is effective for environment perception in a robotic system.

Cited by 0

An Object Model for Sensor Data Integration

Mark Roantree, John Murphy · 2008

One example of large volumes of distributed data is that of sensor networks, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In areas such as Personalised Health, large volumes of data are generated to provide an early indication of potential health problems. Our research is focused on sensor data that has been enriched in the form of XML trees, so that each sensor device can be regarded as an XML tree, and in some cases, containing services. While this architecture offers a certain degree of interoperability, current Web Services approaches are not suited to the difficult tasks of integration, restructuring of information and global issues such as updates. While a Web Services approach offers the advantage of loose coupling of data sources, a more formal model is required to underpin the sensor network. In this paper we describe our Object-Reference model (ORef), which extends a standard object-oriented model with references as the sole concept for locating objects (sensor objects) and their properties, and where the basic object-oriented modelling primitives are respecified to incorporate references. References are also used to define transformations present in the model in the sense that they are closed, meaning that each transformation operates on one or more objects, and results in one or more objects. The purpose of this model is to provide a stable bedrock for the loose coupling of data sources and services in an XML sensor network. 1

Cited by 0

FUZZY DISTANCE SENSOR DATA INTEGRATION AND INTERPRETATION

Zoe Falomir, Vicent Ortiz Castelló, M. Teresa Escrig et al. · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2011

An approach to distance sensor data integration that obtains a robust interpretation of the robot environment is presented in this paper. This approach consists in obtaining patterns of fuzzy distance zones from sensor readings; comparing these patterns in order to detect non-working sensors; and integrating the patterns obtained by each kind of sensor in order to obtain a final pattern that detects obstacles of any sort. A dissimilarity measure between fuzzy sets has been defined and applied to this approach. Moreover, an algorithm to classify orientation reference systems (built by corners detected in the robot world) as open or closed is also presented. The final pattern of fuzzy distances, resulting from the integration process, is used to extract the important reference systems when a glass wall is included in the robot environment. Finally, our approach has been tested in an ActivMedia Pioneer 2 dx mobile robot using the Player/Stage as the control interface and promising results have been obtained.

Cited by 5Open access

Improving sensor data analysis through diverse data source integration

Jennifer Casper, Ronald Albuquerque, Jeremy Hyland et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009

Daily sensor data volumes are increasing from gigabytes to multiple terabytes. The manpower and resources needed to analyze the increasing amount of data are not growing at the same rate. Current volumes of diverse data, both live streaming and historical, are not fully analyzed. Analysts are left mostly to analyzing the individual data sources manually. This is both time consuming and mentally exhausting. Expanding data collections only exacerbate this problem. Improved data management techniques and analysis methods are required to process the increasing volumes of historical and live streaming data sources simultaneously. Improved techniques are needed to reduce an analysts decision response time and to enable more intelligent and immediate situation awareness. This paper describes the Sensor Data and Analysis Framework (SDAF) system built to provide analysts with the ability to pose integrated queries on diverse live and historical data sources, and plug in needed algorithms for upstream processing and filtering. The SDAF system was inspired by input and feedback from field analysts and experts. This paper presents SDAF's capabilities, implementation, and reasoning behind implementation decisions. Finally, lessons learned from preliminary tests and deployments are captured for future work.

Cited by 1

Secure sensor data management model in a sensor - cloud integration environment

Sayantani Saha · 2015

This paper describes the architecture and design of WSN based applications integrated with cloud based delivery model for a fast and reliable data transmission to the end users. The operational challenges of Wireless Sensor Network (WSN) have increased the need for additional resource support from different paradigms. Cloud computing has emerged as a powerful backbone for providing resource support for better computation, processing and communication. Inheriting the advantages from both the WSN and Cloud Computing [CC], we propose here a conceptual architecture for a fast and secure sensor data processing model. Our proposed model helps in managing and accessing sensor data efficiently with respect to processing, communication and security perspective.

Cited by 17

A substrate for in-network sensor data integration

Svilen R. Mihaylov, Marie Jacob, Zachary G. Ives et al. · 2008

With the ultimate goal of extending the data integration paradigm and query processing capabilities to ad hoc wireless networks, sensors, and stream systems, we consider how to support communication between sets of nodes performing distributed joins in sensor networks. We develop a communication model that enables in-network join at a variety of locations, and which facilitates coordination among nodes in order to make optimization decisions. While we defer a discussion of the optimizer to future work, we experimentally compare a variety of strategies, including at-base and in-network joins. Results show significant performance gains versus prior work, as well as opportunities for optimization.

Cited by 16

Sensor data integration and management of smart environments

Helder Moreira · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2016

Num mundo de constante desenvolvimento tecnológico e acelerado crescimento populacional, observa-se um aumento da utilização de recursos energéticos. Sendo os edifícios responsáveis por uma grande parte deste consumo energético, desencadeiam-se vários esforços de investigações de forma a criarem-se edifícios energeticamente eficientes e espaços inteligentes. Esta dissertação visa, numa primeira fase, apresentar uma revisão das atuais soluções que combinam sistemas de automação de edifícios e a Internet das Coisas. Posteriormente, é apresentada uma solução de automação para edifícios, com base em princípios da Internet das Coisas e explorando as vantagens de sistemas de processamento complexo de eventos, de forma a fornecer uma maior integração dos múltiplos sistemas existentes num edifício. Esta solução é depois validada através de uma implementação, baseada em protocolos leves desenhados para a Internet das Coisas, plataformas de alto desempenho, e métodos complexos para análise de grandes fluxos de dados. Esta implementação é ainda aplicada num cenário real, e será usada como a solução padrão para gestão e automação num edifício existente.

Cited by 0Open access

Scalable integration and pre-processing of sensor data streams

Tello Guerrero, Marco Andr?s · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2015

En la presente tesis se propone una soluci?n para integrar datos provenientes de redes de sensores que est? basada en patrones de integraci?n de datos (EIP), una middleware orientada a mensajes (MOM) y un sistema de procesamiento distribuido de streams (SPS). Se presenta una definici?n formal de los problemas que se encuntran al integrar y pre-procesar los datos provenientes de sensores. Adicionalmente, se presenta un prototipo de nuestra soluci?n, implementado en Java y utilizando las herramientas de software libre Apacha Kafka y Apache Storm. Finalmente, se eval?a nuestra soluci?n en t?rminos de escalabilidad y exactitud usando diferentes escenarios. Las pruebas demuestran que la combinaci?n de Kafka y Storm permite crear aplicaciones escalables; y que en estos escenarios en particular, se observ? un incremento en rendimiento de cerca del 75%.

Cited by 0Open access

mHealth4Afrika: Supporting Standards Based Integration of Medical Sensor Data

Mert Baskaya, Mustafa Yüksel, Gökçe Banu Laleci Ertürkmen et al. · Studies in health technology and informatics · 2019

mHealth4Afrika has introduced the use of CE approved medical sensors at the point of care in primary healthcare facilities in Africa as part of an integrated platform supporting primary health care services. This paper shares insights into the standards-based architecture and HL7 FHIR service developed to support data transfer from sensors with proprietary standards to populate the mHealth4Afrika electronic patient record via custom Android and Windows applications. The current iteration is being validated in healthcare facilities in Ethiopia, Kenya, Malawi and South Africa.

Cited by 1Open access