Global Sensor Networks

The availability of cheap and smart wireless sensing devices provides unprecedented possibilities to monitor the physical world. On the technical side these devices introduce several original research problems, many of them related to the integration of the rampant technology proposals. Global Sensor Network (GSN) is a platform which provides a scalable infrastructure for integrating heterogeneous sensor network technologies using a small set of powerful abstractions. GSN supports the integration and discovery of sensor networks and sensor data, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation through a declarative XML-based languag

The Global Sensor Networks middleware for efficient and flexible deployment and interconnection of sensor networks

The lack of standardization and the continuous inflow of novel sensor network technologies have made their deployment the main factor of manpower consumption, considerably complicate the interconnection of heterogeneous sensor networks, and make portable application development a challenging and time-consuming task. To address these problems we propose our Global Sensor Networks middleware which supports the rapid and simple deployment of a wide range of sensor network technologies, facilitates the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN offers virtual sensors as a simple and powerful abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. The paper describes GSN's conceptual model and system architecture, and demonstrates the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://globalsn.sourceforge.net/

A Data Stream Publish/Subscribe Architecture with Self-adapting Queries

Abstract. In data stream applications, streams typically arise from a geographically distributed collection of producers and may be queried by consumers, which may be distributed as well. In such a setting, a query can be seen as a subscription asking to be informed of all tuples that satisfy a specific condition. We propose to support the publishing and querying of distributed data streams by a publish/subscribe architecture. To enable such a system to scale to a large number of producers and consumers requires the introduction of republishers which collect together data streams and make the merged stream available. If republishers consume from other republishers, a hierarchy of republishers results. We present a formalism that allows distributed data streams, published by independent stream producers, to be integrated as views on a mediated schema. We use the formalism to develop methods to adapt query plans to changes in the set of available data streams and allow consumers to dynamically change which streams they subscribe to.

Integrating distributed data streams

Abstract unavailable please refer to PD

Design and implementation of an efficient data stream processing system

In standard database scenarios, an end-user assumes that all data (e.g., sensor readings) is stored in a database. Therefore, one can simply submit any arbitrary complex processing in the form of SQL queries or stored procedures to a database server. Data stream oriented applications are typically dealing with huge volumes of data. Storing data and performing off-line processing on this huge dataset can be costly, time consuming and impractical. This work describes our research results while designing and implementing an efficient data management system for online and off-line processing of data streams in the field of environmental monitoring. Our target data sources are wireless sensor networks. Although our focus is on a specific application domain, the results of this thesis are designed in a generic way, so that they can be applied to wide variety of data stream oriented applications. This thesis starts by first presenting the state-of-the-art in data stream processing research specifically window processing concepts, continuous queries, stream filtering query languages and in-network data processing (particular focus on TinyOS-based approaches). We present key existing data stream processing engines, their internal architecture and how they are compared to our platform, namely Global Sensor Network (GSN) middleware. GSN middleware enables fast and flexible deployment and interconnection of sensor networks. It provides simple and uniform access to a comprehensive set of heterogeneous technologies. Additionally, GSN offers zero-programming deployment and data-oriented integration of sensor networks and supports dynamic re-configuration and adaptation at runtime. We present the virtual sensor concept, which offers a high-level view of arbitrary stream data sources, its powerful declarative specification and query tools. Furthermore, we describe design, conceptual, architectural and optimization decisions of GSN platform in detail. In order to achieve high efficiency while processing large volumes of streaming data using window-based continuous queries, we present a set of optimization algorithms and techniques to intelligently group and process different types of continuous queries. While adapting GSN to large scale sensor network deployments, we have encountered several performance bottlenecks. One of the challenges we faced was related to scalable delivery of streaming data for high data rate streams. We found out that we could dramatically improve the performance of a query processor by performing simple grouping of user queries hence sharing both the processing and memory costs among similar queries. Moreover, we encountered a similar performance issue while scheduling continuous queries. Problem of efficiently scheduling the execution of continuous queries with window and sliding parameters is not addressed in depth in literature. This problem becomes severe when one considers large volumes of high data rate streams. In these cases, an efficient query scheduler not only increases the performance at least by an order of magnitude but also, decreases the response time and memory requirements. Finally, we present how our GSN platform can get integrated with an external data sharing and visualization framework namely Microsoft's SenseWeb platform. Microsoft's SenseWeb platform, provides a sensor network data gathering and visualization infrastructure which is globally accessible to the end users. This integration (which is initiated by the Swiss Experiment project and demanded by GSN users) not only shows the scalability of GSN platform when combined with optimized algorithms, but also demonstrates its flexibility

Specifying Single-user and Collaborative Profiles for Alerting Systems

The 21st century is the age of information overload. Often, humans are incapable of processing all of the information that surrounds them and determining its relevance. The impact of overlooking crucial information ranges from annoying to fatal. Alerting systems help users deal with this vast amount of information by employing a push-based rather than a pull-based approach to information delivery. In this way, users receive the information they require at the appropriate moment. Users specify their alerting needs in a profile that is subscribed to the alerting system. The alerting system is continuously fed with data, and filters this data against all subscribed profiles. Whenever incoming data matches a profile, the subscriber is alerted. Although alerting systems solve the problem of information overload, the potential of these systems has not been fully put into practice. Alerting systems are either realised as dedicated systems that, at best, offer a set of possible profiles to choose from or, at worst, offer a preset profile for one purpose only. Alternatively, they are application frameworks that offer no support for the average user; that is, the specification of profiles is realised using a programming interface. Collaboration between users when specifying profiles is not supported. This thesis verifies the described situation by considering the example application domain of health care. Within this context, a requirements analysis was undertaken involving a patient-based online survey and interviews with health care providers. This analysis revealed the utility of alerting systems but a need for support for profile specification by end-users. It also identified the need for such a system to support the collaborative nature of health care. The shortcomings of alerting systems identified for the health-care area also exist in other domains. Hence, a variety of application areas will benefit from providing universal solutions to eliminate these shortcomings. Based on these findings, this thesis proposes the graphical profile specification language GPDL and an interactive single-user software tool that supports its use (GPDL-UI). The thesis introduces a novel collaborative alerting model for Information Systems. A collaborative extension of GPDL is implemented in the software tool CoastEd, an editor for the graphical specification of collaborative profiles. The developed languages and software tools target average users who have no expertise in specifying profiles involving logics and temporal constraints. The efficacy of the proposed languages and software were evaluated through three user studies. The first study examined interpretation and specification with GPDL. Based on the results of this first study, the single-user system GPDL-UI was designed and implemented and then evaluated in a second study. In turn, the lessons learned from the implementation and user studies for the single-user system influenced the development of the collaborative approach CoastEd; this editor was evaluated in the third study. The studies have shown that GPDL and GPDL-UI are suitable means for average users to effectively specify profiles in single-user alerting systems. High levels of accuracy were reached for specification and interpretation in both studies. GPDL-UI turned out to be a usable and effective software tool. The collaborative approach and CoastEd succeed in conveying the idea of collaborative profile specification to average users. Most types of collaborative profiles were successfully specified by users. For the initiator of the collaborative profile specification process, two types of profiles call for further research. Overall, the approach, languages and software tools developed are shown to be effective and merit future research in that area