Semantic Sensor Information Description and Processing

Vincent Huang Mobile and Pervasive Networking

Ericsson Research, Ericsson AB, Sweden vincent.a.huang@ericsson.com

Muhammad Kashif Javed

Department of Computer and System Sciences (DSV)

Royal Institute of Technology, Sweden kash-jav@dsv.su.se

Abstract

Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a Semantic Web Architecture for Sensor Networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic web technologies can provide high level information extraction and inference of sensor data.

1. Introduction

The presence of WSNs in our surrounding environment is increasing day by day. The WSNs are deployed for environmental monitoring, vehicle traffic monitoring, building surveillance, automated meter reading and countless other context-aware applications which automatically adapt to their surroundings. These WSNs continuously generate various volumes of raw data which are normally processed by their associated often customized applications. WSNs are typically deployed as vertical solutions allowing no communication across multiple WSNs. The lack of common and standardized technology for different WSNs further limits the reusability of sensor data. To facilitate the global collaboration and sensing, the Ericsson CommonSense project has envisioned a service oriented architecture (SOA) [1]. The architecture is mapped to three technology planes: Communication Services, Application

Enablers, and Applications. Applications are built using common service blocks residing in the Application Enabler plane. The application enablers include i.e. identification services, security services, and information processing services, etc. Semantic Web technologies have been proposed for the information processing services [2]. In this paper, we study specifically the information processing service enabler through a specific scenario and demonstrate that this can be achieved using semantic web technologies. The first step is to standardize the sensor data representation and attached semantics to it. Defining semantics will enable sharing of sensor data from various WSNs in a meaningful way and for the system to process sensor data from heterogeneous sources. Two different aspects of this problem are addressed: 1. how to describe sensor data so that it becomes meaningful and 2. how to process sensor data so that high level information can be extracted and shared among different applications.

We propose an architecture for sensor information description and processing and study the role of semantic web technologies as enablers.

The organization of this paper is as follows: Section 2 covers the background information on sensor networks and the semantic web framework. Section 3 highlights some related work. In section 4, we propose an information processing architecture for sensor networks using semantic web technologies as enablers. Section 5 and 6 discuss the implementation of sensor description and processing in our specific use cases. The concluding remarks are given in Section 7.

2. Background Sensor Network

A wireless sensor network (WSN) is a group of specialized transducers with a communication infrastructure intended to monitor physical phenomena like temperature, sound, light intensity, location, motion of objects and so on. Each sensor network is deployed to

The Second International Conference on Sensor Technologies and Applications

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DOI 10.1109/SENSORCOMM.2008.23

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The Second International Conference on Sensor Technologies and Applications

978-0-7695-3330-8/08 $25.00 © 2008 IEEE

DOI 10.1109/SENSORCOMM.2008.23

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serve a specific purpose and uses its own protocols. This heterogeneity in sensor networks makes it impossible to communicate with each other or to reuse and share their data with different applications.

The term WSN is used since most sensors are connected using wireless technologies such as Zigbee, Bluetooth and Wibree. In the context of this paper we don’t make any assumptions about the underlying sensor data transport mechanism and as a result wired sensors can be equally represented in our system. The typical architecture of WSN consists of several nodes communicating with the outside world through a gateway as shown in Figure 1.

Figure 1 Wireless Sensor Network

Sensor nodes are responsible for detecting and monitoring of certain phenomena and sending the raw measurement data to an end user. Further analysis and processing of sensor data is application dependent. Semantic Web Framework

Data integration of the sensor information is the most challenging task especially in case of heterogeneous data sources like WSNs. Sheth [3] have identified four different levels of heterogeneity.

• System (different hardware and operating systems)

• Syntax (different languages and data representations)

• Structure (different data models) • Semantics (different meaning of terms in special

context) Technologies on different system levels like CORBA1,

DCOM2, and various middleware products have been developed to overcome this problem of heterogeneity. XML has gained a wider acceptance as a method of providing common syntax for exchanging heterogeneous information [4]. XML was designed to describe data and is used to structure, store and send information in a user defined way. The data in an XML document are stored inside a balanced tree of open and close tags. Each tag contains a number of attribute-value pairs. The OGC

1CORBA: Common Object Request Broker Architecture 2 DCOM: Distributed Component Object Model

Sensor Web Enablement [5] initiative is also another step towards integrating data from various sensor networks to deploy sensor web applications over the internet and is based on XML. But unfortunately, XML does not provide any means to attach semantics (meanings) of data.

The Semantic Web initiative was taken by World Wide Web Consortium [6]. The semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries [7]. The semantic web extends the idea of web applications to an integrated web of data, which can be effectively shared by different users and can be easily processed by machines as well.

The semantic web is based on the Resource Description Framework (RDF)3. Semantic Web allows people to express, in a machine-processable form, the relationship between different sets of data and their properties. Thus, establishing a “semantic link” between data from difference sources, this allows machines to automatically understand data from many heterogeneous sources and thus be able to process and infer new information. Ontology

At the heart of semantic web framework is the concept of ontologies. An ontology is defined as an explicit and formal specification of conceptualization [8]. Guarino [9] had identified four types of ontologies, namely top-level, domain, task and application ontologies. Ontologies serve the purpose of content explication [10]. So, we need sensor ontology for defining semantics of sensor data. The Sensor Standard Harmonization Work Group (SSHWG) is in the process of study whether ontology is needed to harmonize different sensor standards [11].

3. Related Work

The WSNs are used for number of purposes. A piece of interesting research work on WSN applications was carried out by Mainwaring et al. [12] on real-world habitat monitoring. They proposed a system architecture for sensor networks to carry out environmental and behavioral monitoring of living beings. However, their research was not oriented towards the data management of sensor networks. Besides real-world habitat monitoring, WSNs are playing an important role in different domains such as Geographical Information Systems (GIS), traffic management, location-based services, Telematics [13][14].

Many researchers have also realized the problem of semantic integration of sensor data and try to address it

3 http://www.w3.org/TR/rdf-syntax-grammar/

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using semantic web technologies. A theoretical discussion has been done by Lionel et al. [15], who proposed a concept of Semantic Sensor Net (SSN). They have identified the same associated problems for heterogeneous sensor networks and sensor data. A SSN is a heterogeneous sensor network which enables dynamic tagging of semantic information to sensor data so that it can be integrated and reused across various applications. Lewis et al. [16] presented the ES3N tool to address the issues of efficient sensor data storage and query processing. Moodley and Simonis [17] proposed the Sensor Web Agent Platform (SWAP) framework to overcome the deficiencies of OGC Web Enablement initiative. Liu and Zhao [18] presented the idea of open sensor-rich information system. They motivate the use of common ontologies to capture the sensor data in hierarchal form so that inferencing can be done on sensor data. Noguchi et al. [19] proposed automatic generation and connection of program components for sensor data processing in network middleware. The raw sensor data is described in RDF and they implemented behavior detection services to validate automatic generation and connection.

The foremost reason behind using semantic web technology is to achieve machine-to-machine communication. We want to embed knowledge in computers to make decisions the same way we humans do. One important limitation is the inability of machines to understand the context of any situation and take decisions accordingly. Context-awareness is a term widely used especially in pervasive computing, to refer to this idea that computers can both sense and react based on their environment. Schilit et al. [20] identified the important aspects of context to be: 1) where you are, 2) who you are with and 3) what resources are nearby. A notable work has been done by Ejigu et al. [21] in which they proposed a collaborative context-aware service platform (CoCA) for pervasive computing. CoCA is based on ontologies to perform reasoning based on context data. 4. Semantic Web Architecture for Sensor Networks (SWANS)

In this paper, we extend our previous study on the CommonSense architecture and focus specifically on the information service enabler. We propose a sensor information processing architecture named as SWASN (Semantic Web Architecture for Sensor Networks). The architecture enables machine understanding of the information carried in the sensor data, and advanced information processing adapted to the requirements of applications. The SWASN architecture is divided into four layers (see Figure 2).

Figure 2 Semantic web architecture for sensor

networks Sensor Network Data Sources: The first layer is the

data source layer which consists of heterogeneous WSNs consisting of different sensor nodes. The data from sensor nodes are gathered and can be accessed through a standard sensor gateway. For a legacy sensor system, the data can have any proprietary format.

Ontology Layer: Once the sensor data is available through a gateway, we need to describe the semantics of this data. Therefore, the second layer of our architecture consists of sensor ontologies. We suggest the hybrid ontology approach [10]. Every WSN can have its own local ontology. The ontology could be inside the gateway or obtained by the CommonSense system offline. The concepts from local ontologies will be described using the global terms defined in the global sensor ontology. This approach provides greater flexibility and extensibility as new WSN can be easily added without any need for modification in other local ontologies or shared vocabulary. The sensor data that comes from the sensor nodes through the gateway, maps to the ontologies in this layer. The ontology layer can also consist of other context-aware ontologies like COBRA-ONT4.

4Context Broker Architecture, COBRA-ONT (Version 0.4 2003-11) http://cobra.umbc.edu/ontologies-2003-11.html

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Semantic Web Processing Layer: The data can be processed after defining semantics of sensor data by mapping it to ontology. The ontology layer interacts directly to the Semantic Web Processing layer which is based on Jena API5. Jena is responsible for creating RDF graphs of the sensor data. The Jena has a core API which can create a simple model in RDF or an ontology model in OWL. The sensor data can be then stored temporarily in the main memory or on a persistent database or file-based system. Jena also provides a SPARQL6 query engine, which is responsible for querying RDF sensor data. Similarly, Jena provides its own reasoning engine as well as generic-rule engine to perform inference over RDF data. For performing more complex reasoning, Jena also provides the facility to attach any external DIG7 Reasoners like Pellet8, Racer9 or FaCT10 through a standard DIG Interface. The rule component contains all the defined rules written in Jena Rule format which is a quite similar to Rule ML11.

Application Layer: The application layer consists of different client applications that require the sensor data. The processed sensor data can be available to those client applications through any web server over the internet/ intranet. The interaction between client applications and web server is through HTTP.

5. Sensor Data Description

We conducted a case study based on location and physiological WSNs. The location WSN maintains the location of persons or objects and provides their position in terms of longitude and latitude values. The physiological WSN measures the Electrocardiogram (ECG) of a person. The ECG WSN consists of just one ECG node that provides the health index which is a certain aggregate value of a given electrocardiogram. This health index determines the condition of the person and its value range can be defined arbitrarily.

To describe the semantics of sensor data, we need to

create an ontology. We used the Noy and McGuniness [22] ontology development guide. We determined the

5Jena - A Semantic Web Framework for Java. http://jena.sourceforge.net/ 6SPARQL Query Language for RDF. http://www.w3.org/TR/rdf-sparql-query/ 7DL Implementation Group. http://dl.kr.org/dig/index.html 8http://pellet.owldl.com/ 9http://www.racer-systems.com/ 10http://www.cs.man.ac.uk/~horrocks/FaCT/ 11 http://www.ruleml.org/

domain and scope of the sensor ontology and identified all the important terms that can be part of the WSNs (e.g. ECG sensor node) and sensor data (e.g. latitude, longitude, timestamp etc). Then we separated concepts from properties and relationships. Concepts become classes and are organized in a class-subclass relation. For instance, ECGNode or LocationNode is a sub-class of SensorNode. We have defined two different kinds of properties. Object Properties like hasSensorNode or hasMeasurement are used for relating different classes. The other types of properties are Data Type Properties which represent the data attributes of classes. For instance, the Measurement class has hasDate property. LocationMeasurement, being a subclass of Measurement inherits these properties and also contains the hasLongitude and hasLatitude property. The domain and range of these properties are also defined accordingly. A part of sensor ontology is shown in Figure 3.

Figure 3 Example sensor ontology

6. Sensor Data Processing

For sensor data processing, we have used the Jena API. The two main aspects of processing that we have covered are sensor data querying and inference.

Sensor Data Querying

Once we defined the ontology for sensor data mapping, we need to define a method for querying the data. SPARQL is the candidate recommendation of W3C for querying RDF/OWL data graphs and designed specifically to support semantic web applications. Once the raw sensor data is transformed into RDF/OWL format, we can use SPARQL to run queries.

For instance, Figure 4 shows the SPARQL code to find all the health index measurements of a person called John.

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Figure 4 Example SPARQL query

The SPARQL query results are returned as an XML document in a format known as “SPARQL Variable Binding Results XML Format”. Thus, we can easily display and access the SPARQL query results over internet.

Sensor Data Inference

Jena's ModelFactory can associate a data set with a reasoner to create an inference graph. The inference graph contains not only the original data but also the additional statements that are derived based on rules or other inference mechanism. We can also perform a number of different operations with inference mechanism like validation of instance data for detecting data consistencies.

Jena also provides a general purpose rule engine. We defined some rules for transitive reasoning as well as automatic additional assertion of new information. Two example rules are given in Figure 5. The rules are used for deriving the health condition of a person based on the health index measurement.

Figure 5 Example Jena rules

We also need to define the knowledge about the

surrounding environment and the most appropriate way is by using an ontology that provides the semantics of context data as well. For that purpose we have added a context-aware ontology as an important component of SWASN's ontology layer.

In our case study, we considered a fire accident scenario in a building. Each person inside the building has one location sensor node and one ECG sensor node. Different sensor gateways inside the building can provide latitude and longitude along with the current health index value of persons. The sensor data is available through these gateways and is converted into RDF/OWL.

In case of a fire inside building, further inference can be performed to determine the exact position of a person inside the building. For instance, the person is located in Room 123 on floor 5 rather than knowing the 76.8877° North latitude and 122.8768° West longitude. Furthermore, the health index value can be shared to rescue services and paramedical staff. Based on that information, they can take decisions regarding for example which person has a more critical condition and who needs help first. Other context related information can also enhance the effectiveness of their decision making. Figure 6 shows the graphical representation of the defined scenario.

Figure 6 Example fire scenario in a building

In another experiment, we used location and

physiological sensor data for a pervasive game demo. The user plays the role of NEO (game character based on movie “The Matrix”). The location WSN provides the location of the user and the ECG WSN provides the user stress level as a function of the health index. As the player gets more stressed and the health index value drops, NEO has to face more Agents (other game characters). This experiment also shows the wide applicability of our proposed semantic web architecture for sensor networks. 7. Conclusions

Wireless sensor networks are heterogeneous in nature. Sharing and reusability of sensor data is not possible without understanding the semantics of the data. In this paper, we studied the information service enabler in our CommonSense system and proposed an architecture for WSNs with semantic web technologies as enablers. Our proposed architecture SWASN provides a system which can understand the sensor information and process sensor information on the semantic level. SWASN includes ontologies to define the semantics of sensor data. The Jena API is used for senor data processing to query sensor data and extract meaningful information through

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inference. We have defined the mechanism for context-aware processing of sensor data in pervasive communications scenarios. Our case study of a fire emergency scenario in a building provides the proof of concept of our proposed architecture. We have successfully decoupled WSNs from applications, which results in a number of different applications sharing common sensor data. This allows different sensor-related service providers to provide more valuable services. We are carrying out more experiments for further improvements of this proposed architecture. In the future, standardization of semantic web technologies and sensor ontologies can greatly help in further solving this problem. References [1] Srdjan Krco, Mattias Johansson, Vlasios Tsiatsis, “A CommonSense Approach to Real-world Global Sensing”, Proceedings of the SenseID: Convergence of RFID and Wireless Sensor Networks and their Applications workshop, ACM SenSys 2007, Sydney, Australia, November 2007. [2] Vincent Huang and Mattias Johansson, “Usage of semantic web technologies in a future M2M communication system”, Proceedings of the 1st European Semantic Technology Conference, Vienna, Austria, May 31 – June 1, 2007. [3] Sheth, A. P. “Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure to Semantics”. In M. Goodchild, M. Egenhofer, R. Fegeas, and C. Kottman, Interoperating Geographic Information Systems 1999, pp. 5-30. Kluwer Academic Publishers. [4] Cui, Z., Jones, D., and O'Brien, P. “Semantic B2B integration: issues in ontology-based approaches”. ACM SIGMOD Record, 31 (1), 43-48. 2002. [5] OGC® Sensor Web Enablement. Botts, M., Percivall, G., Reed, C., and Davidson, J. “OGC® Sensor Web Enablement: Overview And High Level Architecture”. July 19, 2006. [6] World Wide Web Consortium (2007). Retrieved December 2007, from World Wide Web Consortium (W3C): http://www.w3.org/ [7] W3C Semantic Web Activity. (2007). Retrieved December 2007, from World Wide Web Consortium: http://www.w3.org/2001/sw/ [8] Gruber, T. R. “A Translation Approach to Portable Ontology Specifications”. Knowledge Acquisition, 199-220, 1993. [9] Guarino, N. “Formal Ontology in Information Systems”. Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001.

[10] Wache, H., Vogele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., et al. “Ontology-based integration of information - a survey of existing approaches”. Proceedings of the IJCAI-01 Workshop: Ontologies and Information Sharing, 2001. [11] Kang Lee, National Institute of Standards and Technology, 100 Bureau Drive, MS#8220 Gaithersburg, MD 20899-8220, USA;. “Sensor standards harmonization-path to achieving sensor interoperability”. IEEE Autotestcon, 381-388, 2007. [12] Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., and Anderson, J. “Wireless sensor networks for habitat monitoring”. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. (pp. 88 - 97), 2002. [13] L E Cordova-Lopez, A Mason, J D Cullen, A Shaw and A I Al-Shamma'a, “Online vehicle and atmospheric pollution monitoring using GIS and wireless sensor networks”, J. Phys.: Conf. Ser. 76 012019 (7pp), 2007. [14] Jung-sick Byun, Woo-Suk Shim, Won-Kee Hong, “WSN-based intelligent telematics system”, ECBS 2006. 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems, 27-30 March 2006, Pages: 2 pp. [15] Lionel, M. N., Zhu, Y., Ma, J., Li, M., Luo, Q., Liu, Y., et al. “Semantic Sensor Net: An Extensible Framework”. In Networking and Mobile Computing Vol. 3619, pp. 1144-1153. Berlin / Heidelberg: Springer, 2005. [16] Lewis, M., Cameron, D., Xie, S., and Arpinar, I. B. “ES3N: A Semantic Approach to Data Management in Sensor Networks”. A workshop of the 5th International Semantic Web Conference ISWC, 2006. [17] Moodley, D., and Simonis, I. “A New Architecture for the Sensor Web: The SWAP Framework”. A workshop of the 5th International Semantic Web Conference ISWC, 2006. [18] Liu, J., and Zhao, F. “Towards semantic services for sensor-rich information systems. 2nd International Conference on Broadband Networks, 2, pp. 967- 974, 2005. [19] Noguchi, H., Mori, T., and Sato, T. “Automatic Generation and Connection of Program Components based on RDF Sensor Description in Network Middleware”. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2008-2014, 2006. [20] Schilit, B., Adams, N., and Want, R. “Context-aware computing applications”. Proceeding of Workshop on Mobile Computing Systems and Applications, pp. 85-90, 1994. [21] Ejigu, D., Scuturici, M., and Brunie, L. “CoCA: A Collaborative Context-Aware Service Platform for Pervasive Computing”. Proceedings of Fourth International Conference on Information Technology, pp. 297-302, 2007. [22] Noy, N. F., and McGuinness, D. L. “Ontology Development 101: A Guide to Creating Your First Ontology”. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05, 2001.

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