52 research outputs found

    The integration, analysis and visualization of sensor data from dispersed wireless sensor network systems using the SWE framework

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    Wireless Sensor Networks (WSNs) have been used in numerous applications to remotely gather real-time data on important environmental parameters. There are several projects where WSNs are deployed in different locations and operate independently. Each deployment has its own models, encodings, and services for sensor data, and are integrated with different types of visualization/analysis tools based on in-dividual project requirements. This makes it difucult to reuse these services for other WSN applications. A user/system is impeded by having to learn the models, encodings, and ser-vices of each system, and also must integrate/interoperate data from different data sources. Sensor Web Enablement (SWE) provides a set of standards (web service interfaces and data encoding/model specifications) to make sensor data publicly available on the web. This paper describes how the SWE framework can be extended to integrate disparate WSN sys-tems and to support standardized access to sensor data. The proposed system also introduces a web-based data visualiza-tion and statistical analysis service for data stored in the Sen-sor Observation Service (SOS) by integrating open source technologies. A performance analysis is presented to show that the additional features have minimal impact on the sys-tem. Also some lessons learned through implementing SWE are discussed

    Sensor Networks and Their Applications: Investigating the Role of Sensor Web Enablement

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    The Engineering Doctorate (EngD) was conducted in conjunction with BT Research on state-of-the-art Wireless Sensor Network (WSN) projects. The first area of work is a literature review of WSN project applications, some of which the author worked on as a BT Researcher based at the world renowned Adastral Park Research Labs in Suffolk (2004-09). WSN applications are examined within the context of Machine-to-Machine (M2M); Information Networking (IN); Internet/Web of Things (IoT/WoT); smart home and smart devices; BT’s 21st Century Network (21CN); Cloud Computing; and future trends. In addition, this thesis provides an insight into the capabilities of similar external WSN project applications. Under BT’s Sensor Virtualization project, the second area of work focuses on building a Generic Architecture for WSNs with reusable infrastructure and ‘infostructure’ by identifying and trialling suitable components, in order to realise actual business benefits for BT. The third area of work focuses on the Open Geospatial Consortium (OGC) standards and their Sensor Web Enablement (SWE) initiative. The SWE framework was investigated to ascertain its potential as a component of the Generic Architecture. BT’s SAPHE project served as a use case. BT Research’s experiences of taking this traditional (vertical) stove-piped application and creating SWE compliant services are described. The author’s findings were originally presented in a series of publications and have been incorporated into this thesis along with supplementary WSN material from BT Research projects. SWE 2.0 specifications are outlined to highlight key improvements, since work began at BT with SWE 1.0. The fourth area of work focuses on Complex Event Processing (CEP) which was evaluated to ascertain its potential for aggregating and correlating the shared project sensor data (‘infostructure’) harvested and for enabling data fusion for WSNs in diverse domains. Finally, the conclusions and suggestions for further work are provided

    The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework, Journal of Telecommunications and Information Technology, 2001, nr 4

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    Wireless Sensor Networks (WSNs) have been used in numerous applications to remotely gather real-time data on important environmental parameters. There are several projects where WSNs are deployed in different locations and operate independently. Each deployment has its own models, encodings, and services for sensor data, and are integrated with different types of visualization/analysis tools based on individual project requirements. This makes it difficult to reuse these services for other WSN applications. A user/system is impeded by having to learn the models, encodings, and services of each system, and also must integrate/interoperate data from different data sources. Sensor Web Enablement (SWE) provides a set of standards (web service interfaces and data encoding/model specifications) to make sensor data publicly available on the web. This paper describes how the SWE framework can be extended to integrate disparate WSN systems and to support standardized access to sensor data. The proposed system also introduces a web-based data visualization and statistical analysis service for data stored in the Sensor Observation Service (SOS) by integrating open source technologies. A performance analysis is presented to show that the additional features have minimal impact on the system. Also some lessons learned through implementing SWE are discussed

    Data transformation and query management in personal health sensor networks

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    Sensor technology has been exploited in many application areas ranging from climate monitoring, to traffic management, and healthcare. The role of these sensors is to monitor human beings, the environment or instrumentation and provide continuous streams of information regarding their status or well being. In the case study presented in this work, the network is provided by football teams with sensors generating continuous heart rate values during a number of different sporting activities. In wireless networks such as these, the requirement is for methods of data management and transformation in order to present data in a format suited to high level queries. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. The challenge arises from the gap that emerges between the low level sensor output and the high level user requirements of the domain experts. This paper describes a process to close this gap by automatically harvesting the raw sensor data and providing semantic enrichment through the addition of context data

    Building a context rich interface to low level sensor data

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    Sensor networks play an important role in our modern information society. These networks are used for a variety of activities in different domains, including traffic monitoring, environmental analysis, transport and personal health. In general, systems generate data in their own format with little or no associated semantics. As a result, data must be managed individually and significant human effort is required to analyze data and develop ad-hoc applications for different end-user requirements. The research presented here proposes a holistic and comprehensive approach to significantly reduce the human effort in analyzing networks of sensors. The goal is to facilitate any form of sensor network, enabling users to combine related semantics with sensor data, and facilitate the end-user transformation of data necessary to provide more complex query expressions, and thus meet the analytical requirements

    Activity Recognition for Smart Building Application Using Complex Event Processing Approach

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    Activity recognition has become one of the most interesting and challenging subjects in performing surveillance or monitoring of smart building system. Although there are several systems already available in the market, limitations and several unresolved issues remain, especially when it involves complex engineering applications. As such, activity recognition is purposely incorporated in the smart system to detect simple and complex events that happen in the building. In all existing event detections, the complex event processing (CEP) approach has been used for the detection of complex events. The CEP is capable of abstracting meaningful events from various and heterogeneous data sources, filtering and processing both simple and complex events, as well as, producing fast mitigation action based on specific scenarios. The work reported in this paper intends to explain in detail on the development of activity recognition application using CAISER™ and NESPER© platform as well as the complex event detection that uses the CEP approach. In assessing the system performance, Matthew Coefficient Correlation (MCC) has been used as the main performance parameter.  Results obtained showed that the Temporal Constraint Template Match Detector (TCD) is more accurate, stable and better in complex event detection compared to NESPER© detector

    A framework for user-assisted knowledge acquisition from sensor data

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    The availability of a new generation of accurate, low-cost sensors to scientists has resulted in widespread deployment of these sensors in a variety of environments. Data generated by these devices are often in a raw, proprietary or unstructured format. As a result, it is difficult for scientists to analyse or query across various biological and physiological sensor data values. There exists both a physical-digital divide between sensor data with related real-world conditions, and a knowledge divide between the information needs of domain specialists. A key challenge is to bridge these divisions in order to allow scientists to make better decisions based on the sensed in- formation. The goal of this research is to show that low level data collection resources such as sensors can be used for high level query expressions and knowledge extraction, without the need for expensive human based operations. To achieve this goal, it was necessary to deliver a generic approach to enriching raw sensor data, providing information services to enable the end user to acquire knowledge from low-level sensor data and defining an integration framework for sensor data and related contextual information. As a result, key research questions of interpreting heterogeneous sensor data, enriching sensor data with contextual information, and integrating sensor data with related participant and environmental information, are tackled over the course of this dissertation

    The DigiHome Service-Oriented Platform

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    International audienceNowadays, the computational devices are everywhere. In malls, offices, streets, cars, and even homes, we can find devices providing and consuming functionality to improve the user satisfaction. These devices include sensors that provide information about the environment state (e.g., temperature, occupancy, light levels), service providers (e.g., Internet TVs, GPS), smartphones (that contain user preferences), and actuators that act on the environment (e.g., closing the blinds, activating the alarm, changing the temperature). Although these devices exhibit communication capabilities, their integration into a larger monitoring system remains a challenging task, partly because of the strong heterogeneity of technologies and protocols. Therefore, in this article, we focus on home environments and propose a middleware solution, called DigiHome, that applies the Service Component Architecture (SCA) component model to integrate data and events generated by heterogeneous devices in this kind of environments. DigiHome exploits the SCA extensibility to incorporate the REpresentational State Transfer (REST) architectural style and, in this way, leverages on the integration of multiscale systems-of-systems (from wireless sensor networks to the Internet). Additionally, the platform applies Complex Event Processing technology that detects application-specific situations. We claim that the modularization of concerns fostered by DigiHome and materialized in a service-oriented architecture, makes it easier to incorporate new services and devices in smart home environments. The benefits of the DigiHome platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations

    The DigiHome Service-Oriented Platform

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    International audienceNowadays, the computational devices are everywhere. In malls, offices, streets, cars, and even homes, we can find devices providing and consuming functionality to improve the user satisfaction. These devices include sensors that provide information about the environment state (e.g., temperature, occupancy, light levels), service providers (e.g., Internet TVs, GPS), smartphones (that contain user preferences), and actuators that act on the environment (e.g., closing the blinds, activating the alarm, changing the temperature). Although these devices exhibit communication capabilities, their integration into a larger monitoring system remains a challenging task, partly because of the strong heterogeneity of technologies and protocols. Therefore, in this article, we focus on home environments and propose a middleware solution, called DigiHome, that applies the Service Component Architecture (SCA) component model to integrate data and events generated by heterogeneous devices in this kind of environments. DigiHome exploits the SCA extensibility to incorporate the REpresentational State Transfer (REST) architectural style and, in this way, leverages on the integration of multiscale systems-of-systems (from wireless sensor networks to the Internet). Additionally, the platform applies Complex Event Processing technology that detects application-specific situations. We claim that the modularization of concerns fostered by DigiHome and materialized in a service-oriented architecture, makes it easier to incorporate new services and devices in smart home environments. The benefits of the DigiHome platform are demonstrated on smart home scenarios covering home automation, emergency detection, and energy saving situations

    JTIT

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