A New Framework for On-Demand Virtualization ... - Semantic Scholar

A New Framework for On-Demand Virtualization, Repurposing and Fusion of Heterogeneous Sensors Yong Liu, David Hill, Alejandro Rodriguez, Luigi Marini, Rob Kooper, James Myers National Center for Supercomputing Applications University of Illinois at Urbana-Champaign 1205 W. Clark St. Urbana, IL 61801, USA {yongliu, djhill1, alejandr, lmarini, kooper, jimmyers}@ncsa.uiuc.edu ABSTRACT This paper describes our first step towards the realization of complex and large scale cross-organization virtual observatories by presenting a new semantically-enhanced “Sensor Network as a Service” (SNaaS) framework, which can repurpose existing sensor networks as needed and aggregate and fuse heterogeneous sensors into new virtual sensors in near-real-time. The architecture of this system allows users to create virtual sensors in a Web 2.0 collaborative map interface. Components of the system are highlighted in the paper including a semantically enhanced streaming data toolkit, virtual sensor ontologies and management middleware. Case studies are presented which can allow users to create new virtual rain gages based on the NEXRAD (Next Generation Weather Radar) data stream with or without in-situ rain gages on demand in a Chicago urban watershed testbed. The resulting virtual sensor data streams then can be published in multiple formats including a SWE-compliant one so that external SWE-compliant users and applications can seamlessly query and integrate them.

KEYWORDS: Sensor Web, Virtual Sensor, Ontology, Streaming Data Management, Data Fusion, Web 2.0

1. INTRODUCTION With the rapid development and deployment of various scales of sensor networks, predictions have been made in a recent Nature article [4] that by the year of 2020, in-situ sensing will measure everything, everywhere. In particular, the earth environment will be intensively monitored

Xiaowen Wu, Barbara Minsker Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign 205 N. Mathews Av. Urbana, IL 61801, USA {wenzi7,minsker}@illinois.edu

through multiscale environmental sensor networks. Such trends have been already confirmed by the various large scale national environmental observatories initiatives in the United States (US) supported by the US National Science Foundation, such as the WATERS (Water and Environmental Research Systems) Network [28], the Ocean Observatory [31], and the National Ecological Observatory Network (NEON) [19]. In addition, there are various sensors and instruments that are already deployed and managed by many federal, state and local government agencies, research institutions [14] and even citizens (e.g., the Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) (http://www.cocorahs.org/) [10,11], which has participating residential backyard rain gages in 38 states across the US as of this writing.). Given the heterogeneity of the deployed sensor networks, it is a tremendous challenge to make effective usage of such resources in an integrated way. Because each managing entity may have its own right and obligation to design how the sensor network works, it is usually not feasible to mandate the same standards across organizational boundaries. However, many sensor-driven scientific applications (especially real-time decision support system) require the use of sensors from multiple agencies for purposes beyond the scope of the original sensor design and deployment, as illustrated in this paper’s case study. Thus, need exists for a systematic methodology through which to virtualize and repurpose sensor networks. Before proceeding with our discussion of the virtualization of sensor networks, it is important to point out a relevant and increasingly popular term—virtual observatory (VO). Coined first by the astronomical

community, VO recently is defined in [2] as follows: “a VO is a system of interoperable, distributed data repositories made accessible through grid and Web services, allowing large and small groups alike to cope with the complexities of Internet access to data and services regardless of scale”. While such definition justifiably emphasizes the importance of accessing distributed data repositories, it fails to explicitly include the sensor network assets owned/operated by different observatory managing entities and neglects the possible interactions with sensors such as sensor planning and tasking which are included by the Open Geospatial Consortium’s (OGC) Sensor Web Enablement (SWE) standards [3]. This paper presents a new framework to create “Senor Network as a Service” (SNaaS) that enables VOs to repurpose the sensing resources. This aligns with OGC SWE vision with additional capabilities. In particular, this paper presents semantic web and Web 2.0 approach for streaming sensor data management, storage and query, virtual sensor ontologies for managing virtual sensor registration, provenance, spatiotemporal and thematic properties, and a scientific workflow toolkit to perform spatial, temporal and thematic transformation of raw sensor data streams. The design of our framework allows flexible implementation of our own system while still maintaining interoperability with other systems, as show in our later examples that the virtual sensor data streams can be published in SWE-compliant data formats and queried by SWE-compliant services. The rest of this paper is organized as follows: Section 2 introduces the concept of virtual sensors and its ontologies. Section 3 presents the architectural components for the SNaaS framework to enable the dynamic creation of virtual sensors. Section 4 presents a case study in the Greater Chicago Metropolitan Area. We offer discussion of results and related work in Section 5 and finally conclude in Section 6 with summarization and recommendation for future work.

2. THE CONCEPT OF VIRTUAL SENSOR AND ITS ONTOLOGIES To fully address the virtual observatory issues, one has to solve many tough problems such as the complexity in semantic heterogeneities, harmonization in security and access policy as well as other cross-organization network constraints. In this paper, we think the first step and a prerequisite to move towards managing a collection of virtual observatory resources is to develop a virtual sensor solution to manage a collection of streaming sensors. This

will lay the foundation for tackling more sophisticated cross-organization use cases. The SNaaS in this paper is centered on the concept of “virtual sensor”, which has been described in the literature for various purposes. In the wireless sensor network domain, virtual sensors are based on computation or aggregation of sensor measurements. Such computation is usually based on standard SQL aggregation queries such as MIN, MAX, or SUM (see, e.g., Global Sensor Network (GSN) by Aberer et al. [1]). Virtual sensors are also used in feedback-control applications along with “virtual actuators” [9]. A virtual sensor network is also proposed to dynamically reconfigure sensor nodes for different purposes [17]. Kabadayi et al. [18] presents a virtual sensor API (Application Programming Interface) for construction management sensor application. However, none of these previous works considered Web 2.0 and semantic web approach for creating a virtual sensor. In this paper, we define the virtual sensor as a product of spatial, temporal and/or thematic transformation of raw or other virtual sensor streaming data with necessary provenance information attached to this transformation. A virtual sensor behaves just like a real sensor, emitting time-series data in specified geographic region with newly defined thematic concepts or observations which the real sensors may not have. In addition, a virtual sensor may not have any real sensor’s physical properties such as manufacturer or battery power information, but does have other properties such as who creates it, what methods are used, and what original sensors it is based on. Our virtual sensor thus, must combine several modular ontologies, similar to the approach in [13], as discussed below. First, a streaming data ontology is developed to model the time aspects of the virtual sensor data stream. Streaming data management is a very challenging task especially given that sensor data streams typically exhibits characteristics such as irregular time intervals, missing data and high frequency [33]. An OWL (Web Ontology Language)-DL (Description Logic) based streaming data ontology graph is shown in Figure 1 and an XML representation of this ontology can be accessed at http://ar1.ncsa.uiuc.edu/StreamingData/DataStreamDL.owl. There has been abundant work on the ontological representation of time series. However, most of the existing work has focused on reasoning about the changing properties of objects (see, e.g. [44], [45], [46], [48]), or keeping track of provenance of streams produced by composition of processes ([47], [49], [50]). The stream data ontology presented here is a top-level ontology for the representation of data in time series as linear sequences of events, and does not include special

provisions for representing provenance of streams, leaving this work to other top-level or domain-specific ontologies. While there exists other ontologies for representation of sequences of events in specific domains (such as [43]), this ontology is completely general and agnostic about the nature of the events. These events are ordered in time by a timestamp whose intended meaning is the time of the occurrence of the event. To represent the timestamps, this ontology leverages the OWL Time ontology (http://www.w3.org/TR/2006/WD-owl-time-20060927/) which allows the use of both points in time and periods of time. Alternatively, timestamps can be represented as ordinals to include cases were the events are part of an order list, with specific positions inside the list but no defined time of occurrence. This model provides a natural way to represent streams of irregular frequency or with missing data. The associated streaming data API implementation which will be discussed further in Section 3 presents a unified interface for querying, replaying and reasoning about the streaming data (e.g., the concept of “most recent” is supported). Each virtual sensor is associated with one unique data stream identified by the stream identifier.

concepts (http://www.opengis.net/gml/) such as gml:location, gml:Point, gml:Polygon and gml:_CoordinateReferenceSystem. For point-based virtual sensor, we reuse WGS84 concepts (http://www.w3.org/2003/01/geo/wgs84_pos#) such as geo:latitude and geo:longitude. Note that a virtual sensor with such explicit geospatial properties is also called virtual geo sensor [24]. Third, for the thematic aspect of the virtual sensor, we define a new concept “hasThematicInterest” which describes a new concept or higher level semantic meaning of the observation produced by the virtual sensor. For example, the National Weather Service (NWS)’s NEXRAD (Next Generation Weather Radar) Level II (base) data measures reflectivity, which can be used to derive rainfall rate and produce new virtual rain gages. Such thematic transformation is often associated with a data fusion process although this is not a required step. Thematic transformation can also represent the derivation of an estimate of an unmeasured process given the raw data and a model. In our current work, the ThematicInterest is a list of values (such as rainfall rate, or rainfall accumulation), which a virtual sensor can pick one. Depending on the need of the thematic interest, a uniform time interval might also be defined to represent the data stream. This is often needed for applications that consume such data streams, thus a concept (hasTemporalInterval) is added. A “belongsToLayer” is also added to represent the virtual sensor as a geographical layer on a Geographical Information System (GIS). This way, we can group virtual sensors into different presentation and analysis layer in the GIS. TemporalFrequency

Layer

hasTemporalInterval

belongsToLayer

derivedFrom

hasDataStream

Virtual Sensor

DataStream (see Fig. 1)

hasLocation hasThematicInterest

SpatialThing isA

Point

isA

Polygon

ThematicInterest

Figure 2. A Segment of the Virtual Sensor Ontology

Figure 1. Streaming Data Ontology for Virtual Sensor Second, for the spatial aspect of the virtual sensor, we reuse the existing GML (Geography Markup Language)

Fourth, for provenance tracking, we leverage the open provenance model (OPM) [30] and use concepts such as “derivedFrom” to note the causal relationships between the raw and/or other virtual sensor and the new virtual sensor. Note that a real sensor can be registered as a virtual sensor without any “derivedFrom” relationship. We also use a scientific workflow toolkit called

CyberIntegrator [26] to execute the spatial, temporal and thematic transformations, during which provenance is also tracked. An example of such transformation workflow can be found in [25]. Figure 2 shows a portion of the virtual sensor ontology. Note that this is not meant to be a completed version of virtual sensor ontology, but rather serves as a starting point for us to leverage semantically enhanced data models for further development. This is also true for the streaming data ontology we developed, which also serves as a streaming data model. Also note that no rule-based reasoning and inference is used in this paper, although this could be a future paper’s topic. Here we also want to briefly compare our virtual sensor concept with other existing sensor standards. According to Chen and Helal [6], sensor standards can cover up to six layers from the physical world to the digital world: physical and environment description, pins and ports, signals and protocols, measurements and commands, networking protocol and configuration, and applications and services. There are different sensor standards depending on which layers they cover. For example, OGC SWE SensorML only covers “measurements and commands”, while some other standards may cover more layers. Our definition of SNaaS falls in the same scope as SensorML, as we virtualize the sensors and sensor networks at the “measurements and commands” layer. Note that the current virtual sensor ontology is not as comprehensive as OGC SWE SensorML, as we focus on the spatial, temporal and thematic transformation aspects of the virtual sensor, rather than the complete description of a sensor. An architectural description of the framework and implementation is presented in the next section. A use case will be presented in Section 4 which describes how virtual precipitation sensor can be generated using this framework.

3. ARCHITECTURAL LAYERS AND IMPLEMENTATION OF THE SNAAS FRAMEWORK Given the definitions of the virtual sensor and associated ontologies, a semantically enhanced SNaaS framework is designed to enable the virtual sensor creation, query and sharing. Such SNaaS leverages OGC SWE standards and services, with additions of Web 2.0 and semantic web approach. Figure 3 shows the conceptual three layers of the SNaaS framework. The bottom layer is the physical layer, where the heterogeneous sensor network resides. We also consider the data logger, remote sensor repositories, and

the data and control interface provided by the native sensors as components in this layer, since many legacy sensor networks still operate in this way [14]. The next layer is the virtual sensor abstraction layer, which contains a set of ontologies within a semantic content repository, which acts as a registry for the virtual sensor and a semantic RDF (Resource Definition Framework) data store for the streaming data and descriptions of the workflows. Additionally, a few RESTful (REpresentational State Transfer) Web services should be present including: services for the registration, creation and query of the virtual sensor in the repository, for the invocation and scheduling of workflow which does the spatiotemporal and thematic transformation, and for the query of streaming data itself given a unique stream ID. The top layer is a Web 2.0 collaboration layer which involves different clients including both SWE SOS clients and other Web 2.0 client such as Google Map-based web interface. An important concept here is the “UserGenerated-Virtual Sensor”, which is similar to the UserGenerated-Contents (http://en.wikipedia.org/wiki/Usergenerated_content) concept used in many Web 2.0 applications such as the Wikipedia. By allowing different users to come to a Web 2.0 Map-based interface and point-and-click to generate a Wikipedia of virtual sensors, scientific community participation and collaboration can be greatly promoted. Web 2.0 Collaborative Interaction Layer • • •

User-Generated-Virtual Sensor (VS) Sharing and publishing VS in SWE and other formats Query VS system with multiple criteria

Virtual Sensor Abstraction Layer • • • • •

Ontologies Semantic content repository RESTful-services to register/query/create VS Dynamic workflow invocation Streaming data management/relay/storage and reasoning

Physical Layer • • •

Heterogeneous sensor network Data and control interface Remote sensor repository/data logger

Figure 3. Conceptual Layers of the SNaaS Framework Prototype implementation of the virtual sensor system just described has been an incremental effort. A previous implementation, which is based on top of the Digital Synthesis Environment (DSE) developed at the National Center for Supercomputing Applications (NCSA), was demonstrated in the recent ACM GIS 2008 conference [25], which highlighted the use of the virtual sensor system by allowing users to point-and-click on a Webbased map to create new virtual sensor instances. This paper presents the next generation of this system, which further integrates 52°North SOS service and client with

enhanced streaming data capability that can fetch and parse legacy rain gage data in real-time. The virtual sensor system consists of the following architectural components: a Google Map-based web interface front end, a virtual sensor middleware layer, a semantic content management middleware called Tupelo (http://tupeloproject.org), and workflow tools and services (CyberIntegrator [26]). Figure 4 shows a schematic view of the architectural components, which roughly correspond to the conceptual layers outlined in Figure 3. The Google Map interface interacts with the workflow service using a RESTful (REpresentational State Transfer) protocol to trigger periodic CyberIntegrator workflow executions. The workflow is implemented to execute the spatiotemporal and thematic transformation needed for creating the virtual sensors. Tupelo data/metadata semantic middleware is implemented using RDF, a W3C semantic web standard. The current virtual sensor middleware is responsible for three tasks: a) registering virtual sensor metadata (such as coordinate information) in the Tupelo-managed registry; b) dynamically triggering back-end workflow execution through the workflow RESTful web service to produce new streaming data; c) dynamically generating an input file needed for the workflow execution which provides a list of virtual sensor coordinates and unique stream IDs. Open Geospatial Consortium (OGC) standard KML (Keyhole Markup Languages) is used to represent the static GIS layers such as the coverage of sensor stations and features of an environmental watershed etc. The current Asynchronous JavaScript and XML (AJAX) front end and virtual sensors middleware are implemented as a combination of jQuery and Google Web Toolkit (GWT) services. Google Map AJAX Interface

In addition, the streaming data tool integrates publish/subscribe systems (called stream managers such as Open Data Turbine [39] and JMS compliant systems such as ActiveMQ) and indexes semantically tagged data points in time-series for efficient streaming. The streaming data tool also supports complex query through RESTful web services such as retrieving data in a user-specified time window, getting the newest or the oldest data point in a stream, obtaining previous or next data point in a userspecified time etc. The query results can be formatted in XML, JSON or CSV, depending on the user’s preference. To enable SWE SOS clients such as 52°North SOS web client to retrieve SWE common data format-compliant observation, additional SOS adapter is implemented so that other necessary meta data can be retrieved from the Tupelo-managed repository and sent to SOS’s query engine so that standard SOS query can be performed.

4. A CASE STUDY In this section, a case study is presented with some results showing the benefit of having a virtual sensor system.

4.1. Background Information about Chicago Testbed To illustrate the benefit of the virtual sensors system, a case study is presented that addresses real-time decision support for the combined sewer system in the Greater Chicago Metropolitan Area. Because these systems rely on a common infrastructure to convey municipal wastewater and stormwater, intense storms can cause wastewater to be discharged untreated into the environment in order to relieve pressure within the sewer system. Such discharges are called Combined Sewage Overflows (CSOs). To help manage CSOs events, a decision support system is being created with models to predict the occurrence of CSOs throughout the sewer system and minimize the effects of CSOs in real-time.

SWE SOS Web Client Virtual Sensor Middleware & RESTful service DSE Core service

CyberIntegrator Execution Service and Scheduling Web Service (CyberIntegrator Workflow tool)

Streaming Data Fetching Streaming Data RESTful Web Service & SOS Data Adapter Tool Tupelo Semantic Content Management Middleware Tupelo-Managed Repository (PostGIS, MySQL, etc.) Sensors Data Loggers

Figure 4. Prototype Implementation of the SNaaS Framework

One key challenge to the development of this decision support system is getting access to rainfall data that are suitable for modeling CSOs. Like urban flooding, CSOs are highly dependent on rainfall variability at the scale on the order of 1 km2. Unfortunately existing rain gage networks are too sparse to measure rainfall at the scales necessary for modeling CSOs. On the other hand, the Next Generation Radar (NEXRAD) system, which is a network of approximately 160 Doppler weather radars operated by the National Weather Service, measures atmospheric reflectivity (a quantity correlated with the precipitation rate) at spatial and temporal scales suitable for CSO modeling. Thus we can repurpose the NEXRAD data to

create virtual rain gages with estimated rainfall by pointand-clicking on the map interface.

4.2. Results Figure 5 shows the screenshots of a virtual sensor created at the location of the Sears Towers in Chicago (the red bubble sits atop the Sears Towers). Once the virtual sensor is setup and running, it starts producing time series data. This time series can be viewed in a pop-up window when users click the virtual sensor marker. We can also query the virtual sensor using the SWE SOS client. Figure 6 shows fragments of the resulting SensorML for the Sears Tower virtual sensor by executing the DescribeSensor query provided by the standard 52°North SOS service. From this result, it can be seen that this virtual sensor is derived from the NEXRAD Level II data from the KLOT radar station and it is actually produced by a workflow. The workflow also has its own URL where it shows the details of the workflow descriptions in an XML file (too large to show in this figure). Such information provides important provenance information regarding the virtual sensor and it is very valuable for community verification if needed (i.e., other users can examine who creates the workflow, what kind of transformation modules they use, etc.).

PREFIX ds: PREFIX geo: PREFIX time:

SELECT ?virtualSensor WHERE { ?virtualSensor vs:hasLocation ?point . ?point geo:lat "41.965277" ; geo:long "-87.881388" . } ------------------------SELECT ?token WHERE { virtualSensor vs:hasDataStream ?dataStream. ?token ds:belongsTo ?dataStream ; ds:hasTimestamp ?time . ?time time:inXSDDateTime ?value . } ORDER BY DESC(?value) LIMIT 10

Note that, however, the streaming data APIs abstract such low level query through an easy-to-use RESTful web service interface. tag:cet.ncsa.uiuc.edu,2008:/VirtualSensor/Sears/r ainfall-rate ...... NEXRAD Level II data from WSR-88D KLOT Doppler radar ...... ...... ......

Figure 6. Fragments of a Virtual Sensor in SensorML after Running SOS DescribeSensor Query

Figure 5. A Screenshot of a Virtual Sensor (the red bubble) in the Google Map Interface and Its Streaming Data Plot To illustrate how the semantics introduced in the virtual sensor ontology can be used, a SPARQL query example is shown below which selects a virtual sensor then selects the 10 most recent readings from that virtual sensor: PREFIX vs:

Since the virtual sensors is not used inside the CSO model (which is not yet fully developed as of this writing), this brings up an interesting question on what the communication API and protocols between the virtual sensor and the model should use once the model is ready. At this moment, we are considering using emerging standards such as OGC Event Pattern Markup Language (http://portal.opengeospatial.org/files/?artifact_id=29566) and OpenGIS® Sensor Event Service Interface Specification (http://portal.opengeospatial.org/files/?artifact_id=29576). Ongoing work also includes fusing the NEXRAD data with data from ground-based in-situ rain gages in real-time and

creating polygon-based virtual sensors. In addition, we will also investigate how to improve the performance and the storage issues raised by continuously arriving data streams into the system (currently we periodically purge the repository as we mainly serve the real-time modeling need.).

5. RELATED WORK AND DISCUSSIONS In this section, we review some related work and offer some discussions.

5.1. “Semantic Sensor Networks”, “Semantic Sensor Web”, and Ontology for Sensors Leveraging semantics and semantic web technology in the sensor network community is an emerging topic. The main motivation is that semantics can potentially facilitate automatic machine-to-machine understanding as well as interoperability among heterogeneous sensors and sensor data. Since at the heart of the semantic web technology is the ontology (which is a formal way to define semantics and relationships), a few references also specifically talk about ontologies for sensors and sensor network. Kim et al. [20] proposed a service-oriented ontology for wireless sensor network which reuses ontologies from OntoSensor (http://www.engr2.memphis.edu/eece/cas/OntoSensor/Ont oSensor), GML, SensorML, and Suggested Upper Merged Ontology (SUMO). The three classes they proposed include ServiceProperty, LocationProperty and PhysicalProperty. Although their focus is not on modeling virtual sensors, their ontology could potentially be harmonized with our virtual sensor ontologies, No streaming data ontology was presented in their paper. The Sensor Standard Harmonization Work Group at the NIST (National Institute of Standards and Technology) is in the process of studying whether ontology is needed to harmonize different sensor standards [22]. A few authors presented work related to semantic sensor networks. Ni et al. [32] offered a theoretical discussion on creating Semantic Sensor Network and enforce semantics in all levels of the sensor network including sensory data, self-organizing system, data dissemination, query processing and services. However, it is unclear what implementation strategy would permit their design. Li and Taylor [23] present a framework for semantic wireless sensor network services, but focus on improving query processing on distributed requests to conserve energy and minimize data transmission. They indicate in their future work they are interested in “providing a powerful ‘virtual device’ (depending on available sensing devices) with

differing sensor capabilities for different tasks (e.g. modeling or tasking)” using OWL ontology approach. Huang and Javed [15] proposed a Semantic Web Architecture for Sensor Networks (SWASN) with a four layer architecture: sensor network data source, ontology layer, semantic web processing layer, and application layer. They did not explicitly model the spatial, temporal and thematic aspects of the “virtual sensor” as we do and there is no workflow or provenance information related. Sheth et al. [34] presented a vision for semantic sensor web and uses RDFa (Resource Description Framework in – attributes, which is a set of extensions to XHTML) to annotate the SWE’s SOS service. Our work aligns with such vision but has a very different focus, virtual sensors and their associated streaming data, and a very different approach, using RDF to directly represent all the data and the metadata in the semantic content repository, which can be queried via a standard SOS service. Dawes et al. [41] presents a Sensor Metadata Repository based on a Semantic Wiki from which they plan to automatically generate the GSN [1] virtual sensor descriptors. However, note that GSN’s virtual sensor is mainly for SQL-style filtering, not the type of spatial, temporal and thematic transformation we do in this paper. A few references talk about integrating multi-agent system to build semantic sensor web. Jabeur et al. [16] presented a comprehensive sensor network cyberinfrastructure vision which they called knowledgeoriented meta-framework to facilitate the goal of getting the right Data from the right Area using the right Sensors at the right Time (DAST Principle). They also proposed a concept of a “Virtual Sensor Network” using an intelligent agents approach. However, such concept is still in the stage of “paper-and-pencil” design and testing. Moodley and Simonis [29] presented another comprehensive multiagent system-based architecture called “Sensor Web Agent Platform (SWAP)”. The SWAP has three layers: sensor layer, knowledge layer and application layer with ontologies and workflows embedded in the layers when needed. A wild fire monitoring application [36] has used the SWAP for its implementation. Although our SNaaS framework does not use any multi-agent system, it is our interest in the future to also explore such integration.

5.2. Other SWE Related Frameworks and Applications There are quite a few non-semantic web SWE-based framework and applications. Delin et al. [51] first proposed the concept of "Sensor Web" in late 1990’s

where the sensor instruments form a "distributed system" that fuses data in-field and builds a virtual sensor (in-field aggregation) as well. Liang et al. [24] presented an early implementation of SWE called GeoSWIFT. A newer version GeoSWIFT 2.0 is recently presented by Liang [42] which adopts a peer-to-peer architecture. Thompson et al. [37] presented a SWE implementation called PULSENet™ for military applications. The developers of the SensorWeb 2.0 middleware (or SensorWeb 2.0 WSN middleware) from the University of Melbourne, Australia are attempting to bridge the gap between sensor web and the grid computing community (note that this platform is also targeting Wireless Sensor Networks with their specific challenges and not just plain web-based Sensor Services.) [7, 21]. Other applications include volcano monitoring [35], the Texas environmental observatory [40], telecare applications [8] etc. Mandl et al. [27] presented an ongoing project at NASA to implement both SWE and workflow services, which serves as a prototype for the Global Earth Observation System of Systems (GEOSS) [5]. Boulay et al. [12] presented a Common Instrument Middleware Architecture (CIMA) middleware which allows remote control and access to instruments in the lab. Their work did not cover any sensor aggregation and fusion, nor do they explicitly use SWE standards since their focus is mainly on controlling scientific instruments in the lab. By leveraging the semantic web technologies and sensor web standards, our semantically enhanced SNaaS framework has a very flexible internal implementation, yet maintains interoperability with the SWE standards.

6. CONCLUSIONS This paper presents a SNaaS framework that allows provenance-aware virtual sensors to be created, managed, and queried on-demand using web 2.0 and semantic web approach and loosely integrated with the standard OGC SWE SOS service. Through a case study, we demonstrate how our virtual sensor system can be used to repurpose and fuse data streams from heterogeneous sensors. Future work will include extending the virtual sensor concept to accommodate virtual sensor networks, and integrating the full suite of the OGC SWE services. This extension will permit sensor planning and tasking based on the topology and functionality of different sensor networks. This improved capability will facilitate adaptive sampling in the broad environmental observatory community. Additionally, by working with the broad sensor web community, we are interested in defining “best

practices” for using sensor web technology, improving interoperability, and advancing the development of virtual observatories to meet emerging challenges, such as those listed in Tilak et al. [38].

ACKNOWLEDGEMENTS The authors would like to acknowledge the Office of Naval Research, which supports this work as part of the Technology Research, Education, and Commercialization Center (TRECC) (Research Grant N00014-04-1-0437) managed by NCSA. We also thank UIUC/NCSA Adaptive Environmental Sensing and Information Systems (AESIS) initiative for funding part of the work. X. W. Wu thanks the graduate research assistantship awarded to her through the Institute for Advanced Computing Applications and Technologies (IACAT). We thank Nicholas Michalski for partially assisting the development of the web user interface. Finally, we would like to acknowledge the entire TRECC team at NCSA working on the Digital Synthesis Framework for Virtual Observatories.

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