The sensor tasking problem in sensor networks involves the representation of users' tasks (user-level tasking) in a form which a sensor network needs to perform required operations (sensor-level tasking) leading to satisfaction of the tasks. We analysed four approaches to task representation (TR) in sensor networks: the Open Geospatial Consortium's Sensor Web Enablement, Goal Lattices, Semantic Streams, and Sensor Assignment to Missions. Each approach considers distinct aspect of the sensor tasking problem. We used the Web Ontology Language, OWL, to de ne the features of each TR, which enabled us then to identify mappings between them. These mappings allow us to combine the TRs into one hybrid task representation (HTR) that addresses both user-level and sensor-level tasking, thus providing a more complete, integrated approach to the sensor tasking problem. In this paper (presented as both poster and demonstration) we introduce our HTR integrated into a system working on a sensor network. It shows how a rich semantic representation of task, such as the HTR, can be used to automatically control system operations on the network, thus making it more adaptable to changes within the state of the network resources (e.g. sensor malfunction) or tasks (e.g. change of a task's requirements).
Sensor networks are becoming increasingly important in many
domains, for example, environmental monitoring, emergency
response, and military operations. There is considerable and
growing interest in developing approaches that allow
sensors to be treated as information-providing resources, and
integrated within Web and Semantic Web information
architectures (for example, [
After a literature review we have identi ed four existing
representations addressing aspects of user-level and/or
sensorlevel tasking, for which there were reasonably detailed
descriptions of the TR formalism: Open Geospatial
Consortium Sensor Web Enablement (SWE) enables
tasking on a sensor-level, allowing for discovery, access and
setting of sensor parameters through Web service standards [
For each TR we have created in Web Ontology Language (OWL1) ontology. The model of hybrid task representation (HTR) was created through alignment of the ontologies integrating the aforementioned capabilities of the four TRs. Fig. 1 shows the mappings which create the HTR ontology. Here we use the following notation: classes are depicted as ovals; subclass relations are shown as unlabelled solid arcs; the OWL sameAs property is represented by a solid bidirectional arcs; all other properties are shown as labelled dashed arcs; we use namespace notation to indicate which TR ontology the concepts are from. The ontologies are available online2. The role of a task representation in a system is to capture \what" and \how" requirements of a user's task in a machine processable form which lets the system gure out how to satisfy the tasks needs. Thanks to the combination of user- and sensor-level TRs and creation of mappings between their concepts using Semantic Web technologies we have obtained an HTR able to express a user's task in human readable terms (e.g. detect a vehicle) that is visible to the sensor-level task representations dealing with setting, collection and processing of sensor information (e.g. camera, radar or acoustic sensor).
Fig.2 shows the interface of a system using the HTR. The top right tree exposes the SAM TR functionality, where the user can select from currently de ned National Imagery Interpretability Rating Scale3Tasks (e.g. detect vehicle) and specify Required Sensing Capabilities in terms of intelligence types (e.g. acoustic, imagery or radar). The bottom tree presents bundles that satisfy a task i.e. platforms with sensors mounted on them, where their combined capabilities are satisfying the requirements of a task. The map serves three purposes: to allow a user to specify the area of a task, to present the location of assets providing some of the capabilities required by a task, and to deliver processed sensor information where appropriate (the jeep icon, representing detected vehicle). The top tab uses the GL TR's capabilities to express relation between tasks, thus prioritising assignment of resources accordingly. The SS & SWE TRs capabilities since they have more to do with the sensor- then user-level tasking are not exposed to the user.The role of SS TR is processing of incoming data from sensors, e.g. from the acoustic array mounted on the PackBot platform, P2, thus pinpointing a detected vehicle (the jeep icon) on the map. Where the exploited functionality of SWE TR is discovery, con guration and use of a network's resources. Other elements of the interface are the mission tab used to switch between missions, and options tab with the application's settings. This interface is presented for a vehicle detection task. It tells the story of what was happening during the execution of this task. The small visible window shows interrupted output that was coming from the camera mounted on the Reaper Unmanned Aerial Vehicle, P1 from the rst listed bundle. In the moment when the signal was interrupted the system automatically switched to an alternative solution, re2http://users.cs.cf.ac.uk/K.Borowiecki/Ontologies.zip 3http://www.fas.org/irp/imint/niirs.htm assigning the resources, by taking the next available bundle with platform P2 containing an acoustic array.
The demo shows that through use of a rich semantic representation of a user's task, which allows a user to state his needs while capturing all information required to operate for the underlying technologies, it is possible to automatically control a system working on a sensor network. In result we obtain a system that is responsive, adaptive and useful in situations or sensor networks where change of sensor and/or tasks state is expected.
Demo Requirements The demo runs on a self-contained laptop but requires an Internet connection.
Acknowledgement This research was sponsored by the U.S.Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF- 06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the o cial policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.